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Open Access Published by De Gruyter May 16, 2015
A Graphical Approximation to Generalization: Definitions and Diagrams
Fernando Martel García and Leonard Wantchekon
From the journal Journal of Globalization and Development
https://doi.org/10.1515/jgd-2014-0013
The fundamental problem of external validity is not to generalize from one experiment, so much as to experimentally test generalizable theories. That is, theories that explain the systematic variation of causal effects across contexts. Here we show how the graphical language of causal diagrams can be used in this endeavour. Specifically we show how generalization is a causal problem, how a causal approach is more robust than a purely predictive one, and how causal diagrams can be adapted to convey partial parametric information about interactions.
Keywords: causal diagrams; econometric methodology; external validity; generalized causal inference; nonparametric methods; policy experiments
JEL classification: B0; C1; C14; C180; C99
The notion of external validity remains highly ambiguous. In their seminal work (Shadish et al. 2002: p. 38) define external validity as the validity of inferences about whether a causal-effect relationship holds over variation in treatments, outcome measures, units, and settings. By variation they mean variation that is within the bounds observed in the original study, as well as variation outside those bounds (Shadish et al. 2002: p. 83–84). By validity they mean "the approximate truth of an inference," adding that validity "is not a property of designs or methods" (their emphasis Shadish et al. 2002: p. 34).
But how are we to judge the approximate truth of an inference (Shadish et al. 2002: p. 35)? Is external validity a function of constant effect sizes (Manski 2007: p. 26), or of constant causal direction (Shadish et al. 2002: p. 91)? Is external validity a concern only when it involves extrapolation (Manski 2007: pp. 26–28)? Finally, should we interpret external validity as claims about the robustness of particular inferences or the generalizability of a theory (Martel García and Wantchekon 2010)? All social scientists are familiar with the quip that "experiments lack external validity." Yet on what basis is this claim made? On the basis that a particular study has not been replicated, or on the basis of theoretical insights connecting features of the original study to new populations of interest?
External validity is about theoretical generalization, or the ability to explain and predict outcomes across variations in treatments, outcome measures, units, and settings. In this study we first make the case for a causal approach to external validity. Implicit in this causal notion of generalization is the idea that all systematic heterogeneity has a causal explanation. That is, asymptotically, once we remove chance variation, all remaining variation in effect sizes is causal in nature. Consequently generalization is but the process of postulating and inferring the causes behind systematic variation in causal effects.
This study introduces a set of structural definitions to better conceptualize and understand generalization. We illustrate how two classes of causal explanations, effect modulation and effect modification, can in principle explain all causal heterogeneity. We also define causal mechanisms, showing how interaction is a functional form property of such mechanisms. And we show that causal generalization is more robust than predictive generalization, or generalization based on correlations devoid of a causal justification. Our humble goal is simply to introduce practitioners to the use of graphical language for generalizability.
Generalization is of great policy relevance, and is central to the scientific enterprise. Given a budget constraint and significant sunk costs most policy makers want to make sure policies shown to be successful elsewhere will also be successful at home. This process might involve meetings of experts to discuss the reasons why the policy may or may not work in the new context, paying special attention to the circumstances where the policy proved successful, how these might differ in the present context, why these differences may modify the effect, and if so how. In effect this amounts to a discussion of the various causes of the outcome. A times the context of other policy interventions might be judged to be so different from the target environment that previous policies are almost irrelevant, leading to what Manski (2007) refers to as predictive ambiguity. But how are those judgements made, and what sort of information would be needed to avoid ambiguity. For example, using selection diagrams Pearl and Bareinboim (2011) and Bareinboim and Pearl (2012) have shown how predictive ambiguity can often be avoided by gathering additional information from the target environment using observational studies.
To advance our understanding of external validity and generalization, and in order to avoid ambiguities, this study relies on the structural causal language of Directed Acyclic Graphs (DAGs). The choice of language is predicated on the fact that external validity, as defined here, is essentially a causal question, and DAGs are specially useful for encoding and communicating researchers' private knowledge about causation. Indeed, it is on the basis of public causal knowledge, as encoded in a DAG, that we can begin to provide unambiguous justifications for why, when, and how a cause may have similar effects in different contexts. Absent this knowledge decisions makers face fundamental uncertainty, and it is anybody's guess whether the policy will work or not.
2 Introduction to Causal Diagrams and Models
This section introduces basic definitions to enable unambiguous talk about generalization. The section may appear somewhat dry but it is critical that these terms be understood. Ultimately there can be no scientific progress if we do not know what we are talking about when we are talking about generalization.[1]
Definition 1 (Graph) A graph is a collection 𝒢=〈V, E〉 of nodes V={V1, …, VN} and edges E={E1, …, EM} where the nodes correspond to variables and the edges denote the relation between pairs of variables.
Definition 2 (Directed Acyclic Graph) A directed acyclic graph (DAG) is a graph that only admits: (i) directed edges with one arrowhead (e.g., →); (ii) bi-directed edges with two arrowheads (e.g., ); and (iii) no directed cycles (e.g., X→Y→X), thereby ruling out mutual or self causation.
A path in a DAG is any unbroken route traced along the edges of a graph – irrespective of how the arrows are pointing (e.g., X M→Y). A directed path, however, is a path composed of directed edges where all edges point in the direction of the path (e.g., X→M→Y is a directed path between the ordered pair of variables (X, Y)). Any two nodes are connected if there exists a path between them, else they are disconnected.
Definition 3 (Causal Structure, adapted from Pearl (2009: p. 44, 203)) A causal structure or diagram of a set of variables W is a DAG 𝒢=〈{U, V}, E〉 with the following properties:
Each node in {U, V} corresponds to one and only one distinct element in W, and vice versa;
Each edge E∈E represents a direct functional relationship among the corresponding pair of variables;
The set of nodes {U, V} is partitioned into two sets:
U={U1, U2, …, UN} is a set of background variables determined only by factors outside the causal structure;
V={V1, V2, …, VN} is a set of endogenous variables determined by variables in the causal structure – that is, variables U∪V; and
None of the variables in U have causes in common.
A causal structure or diagram provides a transparent graphical language for communicating our private knowledge about what variables we believe are relevant for a specific causal analysis, and how these variables stand in causal relation to one another. Figure 1, adapted from (Morgan and Winship 2012, fig. 6), is an example of a causal diagram. In this causal diagram variables Ui are the unobserved background variables, and all other variables are the endogenous variables in V (i.e., they all have at least one arrow pointing into them).[2] By convention solid nodes represent known and measured variables, whereas empty nodes depict unmeasured ones.
Panel (a) depicts a non-parametric structural equation model explaining how test scores (Y) depend on student ability (A), feelings of self-worth (S), neighborhood (N), parental education (P), and unobserved background causes (UY). Exposure to charter schools (C) is caused by ability, parental education, and unobserved background causes (UC); and it affects test scores via feelings of self-worth, a mediator. Panel (b) is the equivalent graphical representation of the non-parametric structural model in Panel (a). The causal diagram contains all the information needed for non-parametric causal identification.
Causal diagram 𝒢 represents a possible theory of causation, one where exposure to charter versus public school (C) affects test scores (Y) via feelings of self-worth (S).[3] At the same time parental education (P) and student ability (A) (unobserved, notice hollow circle) are both common causes of exposure to charter schools, and of test scores. These two causes act as potential confounders. They both imply an association between charter schools and test scores even if charter schools are without effect (e.g., even if we delete the arrows in C→S, or S→Y from 𝒢). Parental education (P) affects tests scores directly, by helping with homework say, and indirectly, via the choice of residential neighborhood (N) and school type (C).
Causal diagrams invite the use of an intuitive terminology to refer to causal relations. In a causal diagram C→S reads "C causes S." We also say that C is a parent of S, and S is a child of C, if C directly causes S, as in C→S. For example, the parents of Y are denoted PA(Y)={P, N, S, A}.[4] Similarly, we say that C is an ancestor of Y, and Y a descendant of C, if C is a direct or indirect cause of Y. Thus, P is both a direct cause of Y, as in C→Y, and an indirect cause, as in C→N→Y. We refer to non-terminal nodes in directed paths as mediators. S is a mediator in the path X→S→Y.
In addition to laying out causal theories graphically, and with intuitive terminology, causal diagrams have two additional properties. First, by Definition 3 a DAG of a set of variables W only qualifies as a causal diagram if it includes all common causes of the variables in W (see point 4 in the definition).[5] This ensures the diagram has some nice properties, including the ability to read conditional independencies directly.[6] For example, the diagram tells us that under the null of no effect (e.g., deleting C→S in causal diagram 1), and conditional on P and A, charter schools and test scores are distributed independent of each other. If C and Y remain associated despite controlling for these variables, then we read that as evidence that they are causally related under the assumptions laid out in causal diagram 1.[7]
Second, the definition of causal diagrams relies on directed edges (e.g., arrows) in place of explicit functional relations to depict causal relations between variables in the graph. This is a feature not a bug. Detailed knowledge about specific functional forms is often completely unnecessary for causal identification. To wit, this diagrammatic representation of functional relations is in accordance with how most people store their causal knowledge. For example, most of us know that smoking causes lung cancer but few, if any of us, know the precise functional relation linking them together.
Figure 1 also shows that every causal model has a corresponding causal diagram (Figure 1a and 1b respectively). A causal model is defined as follows:
Definition 4 [Causal Model, adapted from Pearl (2009: p. 203)] A causal modelM replaces the set of edges E in a causal structure 𝒢 by a set of functions F={f1, f2, …, fN}, one for each element of V, such that M=〈U, V, F〉. In turn, each function fi is a mapping from (the respective domains of) Ui∪PAi to Vi, where Ui⊆U and PAi⊆V/Vi and the entire set F forms a mapping from U to V. In other words, each fi in
υi=fi(pai, ui),i=1, 2, …, N,
assigns a value to Vi that depends on (the values of) a select set of variables in V∪U, and the entire set F has a unique solution V(u).
Like the causal diagram, a causal model is completely non-parametric. For example, casual model 1a specifies that being exposed to a charter schools is a function c=fc(a, p, uc). This function is compatible with any well-defined mathematical expression in its arguments like c=α+β1a+β2p+uc, or c=α+β1a+β2p+β3a×p+uc.
Causal models, like causal diagrams, are completely deterministic: Probability comes into the picture through our ignorance of background conditions U, which we summarize using a probability distribution P(u). In turn, P(u) induces a probability distributions P(v) over all endogenous variables in V.[8]
Definition 5 [Probabilistic Causal Model, Pearl (2009: p. 205)] A probabilistic causal model Γ is a pair 〈M, P(u)〉, where M is a causal model and P(u) is a probability function defined over the domain of U.
Finally, social scientists often talk about generalizability in terms of causal mechanisms. But what are causal mechanisms, and what is the difference between a model and a causal mechanism, if any? The present framework allows us to define such mechanisms precisely:
Definition 6 (Causal mechanism) A causal mechanism is any F′⊆F, where F is the set of functions in causal model M=〈U, V, F〉.
For example, in Figure 1a function fy is a causal mechanism generating y, and so too is the set F of all mechanisms in model M. The difference between these two mechanisms is that fy takes some endogenous variables as inputs, whereas mechanism F takes only background variables as inputs. For instance, in causal model 1a we say that ability (A) causes test scores (Y) via mechanism FA,Y={fc, fs, fy}.
After this brief introduction to causal diagrams we turn to the formal definitions needed to understand interventions, heterogeneity, and generalization.
3 Intervention, Causal Heterogeneity, and Generalization
In this section we investigate effect heterogeneity ignoring causal identification issues. We start by laying out the notion of an intervention, then we examine the nature and causes of causal heterogeneity. Throughout we assume a perfectly randomized controlled experiment in one setting, and then consider why and how the exact same intervention may have different results in different settings.
3.1 Intervention
To continue with the charter schools example suppose causal diagram 1 in Figure 1 is a faithful representation of all that we know at time t about the effect of charter schools (C, versus public schools) on test scores (Y), and of possible confounders of this effect like parental education P and student ability A. Testing and estimating the effect of C on Y with observational data is complicated by the fact that student ability is both a confounder and unobserved, so we cannot control for it. Consequently we decide to carry out a randomized controlled trial on a convenience sample S from the population of interest P. In particular, an equal number of students in S are randomly assigned to public schools and the rest to charter schools.
Figure 2 is the intervention equivalent of Figure 1 assuming c is under the complete conrol of the researcher. We call this an intervention do(c) and what it does is replace the second equation in causal model 1a with c=c′, generating the new intervention causal model 2a in Figure 2. In effect experimental intervention deletes all arrows pointing into C, thereby eliminating any possibility of confounding. If the randomized controlled experiment is well implemented any endline association between c and y among the set of expermental subjects S cannot be due to some unobserved cause in common, or confounder, but to a causal effect of c on y.[9]
Causal diagram and model representing an intervention whereby a researcher is completely able to set variable C to any desired value, like c′. Graphically this kind of control is represented by deleting all arrows pointing into C, which captures the idea that nothing causes C other than the researcher's intervention. In terms of the causal model the intervention "wipes out" fc by forcing the value of c′.
Experimental outcomes are uncertain. The experimenter sets the value of C but Nature sets the value of all other background variables U. In effect, an experiment is a probabilistic causal model (see Definition 5) Γ=〈M, PS(u, c)〉, where M is intervention causal model 2a in Figure 2, and PS(u, c) is the joint distribution of background variables u and intervention variable c in sample S. By randomization PS(u, c)=PS(u)PS(c). Nature then solves this probabilistic model and yields the intervention distribution PS(y|do(c)) defined over each randomized level of c∈{c′, c″}.[10] This distribution describes the outcome data from the experiment, and it can be queried to calculate any quantity of interest 𝒬(Γ). For example, for any two distinct values c′ and c″ of c, the average treatment effect is defined as:
Q(Γ)=E[y|do(c′)]−E[y|do(c′′)].
Suppose 𝒬(Γ) is statistically and substantively significant. How can we use this information to predict 𝒬(Γ) in a different sample S* from the same population P (e.g., S*⊆P)?[11] Moreover, what factors can give rise to systematic difference across samples? And what might be the causes of heterogeneity in the causal model?
3.2 Heterogeneity
We begin this section with some intuition, and then follow with some formal definitions needed for the analysis of heterogeneity.
3.2.1 Intuition
Structural causal models are completely non-parametric and potentially heterogeneous. To begin with, consider the sub-model C→S←US of causal diagram 2 in Figure 2. Because C is under the direct control of the researchers, all variation in S across different samples will come from the background variable US. This can be problematic for two reasons. First, variables C and US may happen to interact in mechanism fs(c, us) (we will define interaction below), in which case 𝒬(Γ) may be sensitive to changes in the distribution of the background variables P(u). If so we say Uy is a moderator of the effect of S on Y. Second, such changes in the distribution of background variables are likely to happen. The original experimental sample S is a convenience sample from population P, and so not representative of all background conditions in the population. Consequently, PS*(c, us) in a new sample S* is very likely to differ from PS(c, us), even if PS*(c)≡PS*(c).[12] In sum, if fs(c, us) involves some interaction, and if PS*(c, us)≠PS(c, us), then changes in background conditions are likely to bring about changes in 𝒬(Γ).
Second, consider the full mechanisms by which C is theorized to exert its causal influence on Y as described by causal diagram 2 in Figure 2. As in the previous example, heterogeneity can arise if US and C interact in mechanism fs, and PS(u, c)≠PS*(u, c) in new sample S*. Heterogeneity can also arise if variable S interacts with any other argument of fy, including variables A, N, P, UY. These variables can all – singly or jointly – moderate the effect of S on Y. Importantly, variables A, N, P are all endogenous, that is determined, at least in part, by PS(u). This is another reason why background conditions matter. Conditioning on observable variables is a way to account for the influence of unobservable background conditions.
In addition to moderators, variable US can also act as a modulator of the effect of C on Y. Modulator because it can regulate the effect of C on Y through its moderator effect on mediator S (assuming it has such a moderator effect). The focus on the total effect of C on Y allows us to introduce meaningful new labels, like modulator, which goes to show how the conceptualization of heterogeneous effects arises naturally from the causal structure.
3.2.2 Formal Definitions
Definition 7 (Causal effect structure) A causal effect structure for the effect of a set of variables X on a set of variables Y in causal model M, is a set of variables EX,Y such that it only includes X, and all descendants of X along all directed paths from variables in X to variables in Y.
For example, the causal effect structure for the effect of C on Y according to causal model 2a is the set EC,Y={C, S}. Conventionally such a set of causes and mediators is what researchers have in mind when they think of "mechanisms," but this is at odds with how we defined mechanisms in Definition 6. Besides, it is easy to see that knowing this "mechanism" is not enough to guarantee replication out of sample. In particular, the faithfulness of the replication may also depend on other causes of Y or S, not in EC,Y, that may interact with the causal effect structure, like variable US and all parents of Y other than S in causal diagram 2.
Definition 8 (Direct causal context) A direct causal context for the effect of one set of variables X on another set of variables Y in causal model M is a a set of variables CX,Y such that:
it excludes the casual effect structure EX,Y;
it includes all remaining parents of Y; and
it includes all parents of all mediator variables in EX,Y.
For example, in causal diagram 2b the direct causal context for the effect of C on Y is CC,Y={A, N, P, US, UY}. Conditioning on this set of variables guarantees replication in any other setting, without committing ourselves to any functional form assumptions about interactions. That is, these variables may, or may not, interact with other variables in the causal effect structure but so long as they are conditioned on, faithful replication is guaranteed. Obviously this conditioning strategy fails if some of these variables are unobserved and have moderator effects. The second instance where conditioning strategies fail is when we are asked to replicate in settings that fall outside the original range of observation.
Definition 9 (Probabilistic direct causal context) A probabilistic causal context for the effect of one set of variables X on another set of variables Y in probabilistic causal model Γ is a distribution P(CX,Y), defined over a direct causal context CX,Y.
Suppose the direct causal context CC,Y in causal diagram 2b is fully observed in sample S as P(a, n, p, us, uy).[13] Now suppose we draw another sample S*⊆P, and we observe values a*, n*, p*, us*, uy* s.t. P*(a*, n*, p*, us*, uy*)>0 but P(a*, n*, p*, us*, uy*)=0. In this case the conditioning needs of the target environment go beyond the conditions available in the source environment [e.g., they are outside the support of P(a, n, p, us, uy)]. Predicting quantities of interest for instances where P(a*, n*, p*, us*, uy*)=0 will require extrapolation or interpolation, namely making some functional form assumptions about all mechanisms that take elements of CC,Y as inputs.[14]
Definition 10 [Interaction (adapted from VanderWeele (2009: p. 864)]. For a given probabilistic causal model Γ, there is said to be an interaction between two or more parents of an effect Y, call them set X and set Z, if the quantity of interest computed from Y, 𝒬(Γ), is such that:
Q[P(Y|do(x′), do(z′)), P(Y|do(x′′), do(z′))]≠Q[P(Y|do(x′), do(z′′)), P(Y|do(x′′), do(z′′))],
for some distinct (possibly vector valued) observations x′ and x″ of X, and z′ and z″ of Z.
Interaction is a functional form property of mechanisms. By the definition of a mathematical function we do not need to know the function itself, only its arguments and the values they take, in order to be able to accurately predict quantities of interest across settings using previous realizations. For example, if we are only interested in studying how the effect of a cause X on an effect Y varies across contexts, then we only need to know the arguments to the derivative of fy* with respect to X, where fy* is the reduced form mechanism for the effect of X on Y. This mechanism is at most a function of variables in causal context CX,Y that interact with variables in causal effect structure EX,Y. That is, the variables needed to fully explain the variation of the effect out of sample is a set H⊆{CX,Y, EX,Y}.
Because interactions are a property of the set of mechanisms F in causal model M, model transformations can be used to limit interactions.[15] But here we must think of transformations as functional form transformations of mechanisms F in model M, and not as simple variable transformations.
Definition 11 (Direct causal context interaction) In considering the effect of a set of variables X on another set of variables Y in model M, we say there is a direct causal context interaction of the effect of X on Y according to quantity of interest 𝒬(Γ) whenever any subset EI of causal effect structure EX,Y, interacts with any subset CI of causal context CX,Y. We refer to the set of interacting sets as IX,Y={EI∪CI}. If there are no causal context interactions IX,Y=Ø.
Being completely non-parametric causal diagrams do not convey any functional form information. One possibility is to expand the notation to convey the location of interacting variables. For example, suppose exposure to charter schools interacts with background conditions US. We might label the variables IC,S={C, US} explicitly in the causal diagram using edges with square (□) origins, as shown in Figure 3, where filled squares refer to observed variables (C), and unfilled ones to unobserved variables (US). Graphically, the process of generalization, or of explaining away heterogeneity, requires abstracting and measuring from background US the observable variables generating the heterogeneity thus replacing the empty square with an empty circle in UY. We call such (semi-parametric) diagrams interaction causal diagrams.[16]
Interaction causal diagram. Direct causal context interactions are denoted with an empty square if unobserved (US), or a full square if observed (UC), where we assume IC,S={C, US} (see Definition 11).
Definition 12 (Robustness) The effect of a set of variables X on a set of variables Y according to quantity of interest 𝒬(Γ) is said to be robust if causal model M admits no causal context interaction for this effect (IX,Y=Ø).
Robustness is a strong but powerful property of some causal models. One that allows the researcher to completely ignore the causal context in predicting a given quantity of interest out of sample. The graphical equivalent is an interaction causal diagram without any square nodes.
3.3 Generalization
The process of generalization involves explaining away causal heterogeneity. Suppose we started with causal diagram 2 in Figure 2, and that repeated experimentation across samples from population P show significant variation in the effect of charter schools (C) on self-regard (S), and hence on test scores (Y). Suppose we observe much less variation in this effect within levels of the residential neighbourhood variable (N), than across levels of it. Could it be that N is a cause of S, that we should replace fs(c, us) with fs′(c, n, us), and that, conditional on N, US no longer modifies the effect of C? It might be that feelings of self-worth are relative to a students neighbourhood, as in feeling privileged to be in a charter school within a poverty ridden neighbourhood. We could carry out a two-way randomization of students to neighbourhoods and schools to test this hypothesis. We might find that the evidence is indeed consistent with mechanism fs′(c, n, us), and that, conditional on N, there is no evidence US interacts with C (or N).
That neighborhood causes feelings of self-worth is one possibility. Another possibility is that N is an effect of US, or, more likely perhaps, that they share an unobserved cause in common (Z); in which case N serves as a proxy for their cause in common. More generally, the knowledge that N correlates with the quantity of interest might seem sufficient to condition and predict effects out of sample. We refer to this as the prediction or robustness approach to generalization. By contrast, generalization offers a theory driven analytical approach to validity (Martel García and Wantchekon 2010).
Generalization differs from pure prediction in two crucial aspects. First, it provides theoretically motivated explanations for the causes of heterogeneity. In effect, the process of generalization involves observation, theorizing, abstracting potential moderators from within the set of background variables, and including them explicit in the model as endogenous variables. The second difference between generalization and the predictive approach is that the former is, at least in principle, more robust than the former. Of course, both causal models and purely predictive ones can be proved wrong by the data. The question is how much more fallible are they. Intuitively, causal explanations are more direct and so more robust. In the previous example, if N is shown to cause S, then heterogeneity of the effect of C on S can still arise if there are more variables amongst the background conditions that interact with C. However, if N is only a proxy for some hidden cause Z in common with US, then heterogeneity in the conditional effect can arise at multiple points. For example, due to interactions between UN and Z, or Z and US, in addition to between US and C. This is three times more opportunities for failure compared to the direct causal explanation.[17]
Finally, if for some reason we are only interested in predicting the effect of C on S out of sample, then we can ignore most other variables in causal diagram 𝒢. That is, the relevant causal context is specific to the causal relation under study. In this instance, pruning 𝒢 can help focus our attention on possible moderators within the background variables in US. Causal diagrams make explicit the relevant causal context to be considered for predicting out of sample.
Few scientists begin an experimental investigation by laying out their best guess about the structure of the causal effect, the causal context, and the likely sources of heterogeneity. With the advent of causal diagrams there is little excuse for this practice, as anyone can draw arrows, circles, and squares. In the interest of generalization we would encourage practitioners to lay out threats to external validity explicitly at the outset of the study design in an interaction causal diagram. This way they can plan in advance what sorts of measurements should be taken for generalization, highlight potential threats to generalization, and suggests what measurements might be taken to predict the effect of intervening in a different context.
In some instances theories or educated guesses might not be available but there might be plenty of data on covariates. In these situations it is natural to search the covariate space for evidence of interactions. This can generate new hypotheses to be tested out of sample, including testing whether these covariates are part of the causal context of effect structure, or only proxies for such variables. We would want to test this because, as already noted, causal knowledge is more robust than knowledge about correlations. Also, the approach we have taken thus far relies mainly on non-parametric stratification, though there is much to be said about using hierarchical models for summarizing the inference, especially when there are numerous strata, or they are thinly populated.
Generalization is key to science yet its meaning remains highly ambiguous. Most extant theories have defined generalization in an ex post fashion, emphasizing whether a particular inference holds out of sample. Such a robustness approach obviates the need for theory driven research, emphasizing instead replications across all imaginable contexts. Building on the analytical approach of Martel García and Wantchekon (2010), and the more recent structural approach of Pearl and Bareinboim (2011), this study argues for a theory driven approach. Specifically, interaction causal diagrams can be used to encode ex ante potential sources of heterogeneity on the basis of existing knowledge and theories; to guide the design of experiments, follow-up experiments and measurements that might be needed to further justify external validity claims; and to communicate simply, clearly, and transparently to the broadest audience possible what the researchers know about the sources of causal heterogeneity. Science is a communal endeavor that ought to begin with clear definitions and accessible language.
Corresponding author: Fernando Martel García, 2800 Woodley Rd. N.W., Apt 324, Washington D.C. 20008, USA, e-mail: fmg229@nyu.edu
We would like to thank Rachel Gisselquist, Miguel Niño-Zarazúa and participants in the UNU-WIDER Project Workshop on Experimental and Non-Experimental Methods in the Study of Government Performance, New York University, August 22–23, 2013 for useful comments and suggestions. All errors are ours.
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Published Online: 2015-5-16
Published in Print: 2015-6-1
©2015, Fernando Martel García et al., published by De Gruyter
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Article A Graphical Approximation to Generalization: Definitions and Diagrams
Fernando Martel García, Leonard Wantchekon 2015
García, Fernando Martel and Wantchekon, Leonard. "A Graphical Approximation to Generalization: Definitions and Diagrams" Journal of Globalization and Development, vol. 6, no. 1, 2015, pp. 71-86. https://doi.org/10.1515/jgd-2014-0013
García, F. & Wantchekon, L. (2015). A Graphical Approximation to Generalization: Definitions and Diagrams. Journal of Globalization and Development, 6(1), 71-86. https://doi.org/10.1515/jgd-2014-0013
García, F. and Wantchekon, L. (2015) A Graphical Approximation to Generalization: Definitions and Diagrams. Journal of Globalization and Development, Vol. 6 (Issue 1), pp. 71-86. https://doi.org/10.1515/jgd-2014-0013
García, Fernando Martel and Wantchekon, Leonard. "A Graphical Approximation to Generalization: Definitions and Diagrams" Journal of Globalization and Development 6, no. 1 (2015): 71-86. https://doi.org/10.1515/jgd-2014-0013
García F, Wantchekon L. A Graphical Approximation to Generalization: Definitions and Diagrams. Journal of Globalization and Development. 2015;6(1): 71-86. https://doi.org/10.1515/jgd-2014-0013
Journal of Globalization and Development
Journal and Issue
Articles in the same Issue
What Can Experiments Tell Us About How to Improve Government Performance?
Experimental and Non-Experimental Methods in Development Economics: A Porous Dialectic
Reflections on the Ethics of Social Experimentation
Ancillary Studies of Experiments: Opportunities and Challenges
Evaluating Antipoverty Transfer Programmes in Latin America and Sub-Saharan Africa. Better Policies? Better Politics?


text:
From Boards of Studies
Faculty and School Boards of Studies terms of reference
The terms of reference for the University Boards of Studies, including their sub-committees.
View more corporate information in Boards of Studies
In order to discharge their statutory obligations, Faculty or School Boards of Studies will be responsible to Senate for the following key functions:
(1) the approval and evaluation of academic strategy for the Faculty or School;
(2) the strategic consideration of initial programme proposals for submission to the Academic Programmes Committee, including the rigour of the underpinning market research, the student choice available and the strategic fit of the proposal. Proposed new doctoral programmes may be considered but must also receive strategic consideration by the University Doctoral Studies Committee prior to submission;
(3) the strategic consideration of proposed collaborative provision, including Institutional Agreements and Exchange Partners, for submission to the Academic Programmes Committee. Proposed new doctoral collaborations (including proposals for new Doctoral Training Entities) may be considered but must also receive strategic consideration by the University Doctoral Studies Committee prior to submission;
(4) the academic standards and quality of the education, teaching and research across the Faculty/School and the dissemination of good practice, with exception of those matters which specifically form the business of the university Boards of Studies (Doctoral) and the University Doctoral Studies Committee. The Faculty or School Doctoral Studies Committee will provide information to the Faculty or School Board of Studies on matters pertaining to the academic standards and quality of doctoral study;
(5) the monitoring and review of departmental implementation of institutional quality assurance and academic frameworks for programmes excluding those relating to doctoral study (which will be the responsibility of the Faculty or School Doctoral Studies Committee);
(6) consideration of delegation of authority to appropriate subject-level fora to approve minor or intermediate changes to units on the recommendation of the Faculty and School Learning, Teaching and Quality Committee;
(7) the monitoring of the progress of individual students except those undertaking doctoral study (including exceptional changes of student circumstances and individual schemes of study and supplementary assessment), under delegated powers from Senate;
(8) the award of taught degrees on the recommendation of Boards of Examiners from Programmes, under delegated powers from Senate;
(9) recission of an original award and grant of a new award, excluding those relating to doctoral study, as a result of either a student academic review or an administrative error identified after the conferment of the original award. This recission may be authorised by the Chair on behalf of the Board under Board of Studies Standing Order 7(f);
(10) the establishment of new academic prices at the level of the Faculty or School, under delegated powers from Senate;
(11) the conduct and outcomes of student academic reviews and investigations of assessment offences, excluding those relating to doctoral study;
(12) the monitoring of student complaints, student academic reviews and investigations of assessment offences, excluding those relating to doctoral study;
(13) the appointment of Directors of Studies and Heads of Group and the establishment of Groups under powers conferred by Ordinances 4 and 5;
(14) the appointment of External Examiners (except where the aggregate term of office exceeds four consecutive years) and Visiting Academic Staff under delegated powers from Senate excluding those relating to doctoral study;
(1`5) the approval of part-time and secondary academic appointments and appointment of postgraduate research students into teaching roles;
(16) establishment, award and grant of Fellowships, Scholarships, Bursaries and other such grants at the level of the Faculty or School;
(17) the receipt and consideration of reports from the Departments in the Faculty;
(18) the approval of the establishment and disestablishment of Research Centres; the approval of the appointment of Directors and Deputy Directors of Research Centres; the approval of the continuation of Centres following review by a Faculty or School Research Committee on a three-year cycle.
Each Board of Studies has the power to establish sub-committees as necessary. Each Board of Studies will routinely establish the following sub-committees:
Learning, Teaching and Quality Committee
Board of Examiners from Programmes
Progression Board of Examiners (in accordance with Ordinance 15 and Regulation 16.5)
The Boards of Studies will also convene meetings (as required) of:
Accreditation Sub-Committee
Review Sub-Committee (in accordance with Regulation 17, Conduct of Student Academic Reviews and Appeals)
Board of Inquiry (in accordance with QA53, Examination and Assessment Offences)
Urgent Business
In accordance with Boards of Studies Standing Order 7(f), the Chair is authorised to act on behalf of the Board during the vacation and at other times to deal with urgent business which requires a formal decision before the next available meeting of the Board, subject to report of any action taken to the next meeting of the Board. The decision will be confirmed by the Chair in writing. This is one the basis that, if any such matter is expected to be of significant interest or the subject of significant debate, members of the Board will be consulted by correspondence before any decision is taken or a special meeting of the Board will be scheduled. The Chair of a Sub-Committee of the Board of Studies may also act on behalf of the Sub-Committee during the vacation and at other times to deal with urgent business on the aforementioned basis. In the case of the Faculty and School Research Students Committee, the urgent business procedure can be used during the vacation and at other times where an urgent decision is required in the interests of the student without detriment to the quality and standards of provision.
Owner: Boards of Studies
Approval Date:
Apprived By: Senate
Date of last review: amended by Senate 7 June 2017.
Faculty and School Research Committees membership

redpajama_set_name: RedPajamaCommonCrawl


text:
Velika nagrada Japonske 2000 je bila šestnajsta dirka Svetovnega prvenstva Formule 1 v sezoni 2000. Odvijala se je 8. oktobra 2000.

Rezultati

Kvalifikacije

|-
! 1
| 3
|  Michael Schumacher
| Ferrari
| 1:35,825
|
|-
! 2
| 1
|  Mika Häkkinen
| McLaren-Mercedes
| 1:35,834
| +0,009
|-
! 3
| 2
|  David Coulthard
| McLaren-Mercedes
| 1:36,236
| +0,411
|-
! 4
| 4
|  Rubens Barrichello
| Ferrari
| 1:36,330
| +0,505
|-
! 5
| 10
|  Jenson Button
| Williams-BMW
| 1:36,628
| +0,803
|-
! 6
| 9
|  Ralf Schumacher
| Williams-BMW
| 1:36,788
| +0,963
|-
! 7
| 7
|  Eddie Irvine
| Jaguar-Cosworth
| 1:36,899
| +1,074
|-
! 8
| 5
|  Heinz-Harald Frentzen
| Jordan-Mugen-Honda
| 1:37,243
| +1,418
|-
! 9
| 22
|  Jacques Villeneuve
| BAR-Honda
| 1:37,267
| +1,442
|-
! 10
| 8
|  Johnny Herbert
| Jaguar-Cosworth
| 1:37,329
| +1,504
|-
! 11
| 12
|  Alexander Wurz
| Benetton-Playlife
| 1:37,348
| +1,523
|-
! 12
| 11
|  Giancarlo Fisichella
| Benetton-Playlife
| 1:37,479
| +1,654
|-
! 13
| 18
|  Pedro de la Rosa
| Arrows-Supertec
| 1:37,652
| +1,827
|-
! 14
| 19
|  Jos Verstappen
| Arrows-Supertec
| 1:37,674
| +1,849
|-
! 15
| 6
|  Jarno Trulli
| Jordan-Mugen-Honda
| 1:37,679
| +1,854
|-
! 16
| 15
|  Nick Heidfeld
| Prost-Peugeot
| 1:38,141
| +2,316
|-
! 17
| 14
|  Jean Alesi
| Prost-Peugeot
| 1:38,209
| +2,384
|-
! 18
| 23
|  Ricardo Zonta
| BAR-Honda
| 1:38,269
| +2,444
|-
! 19
| 17
|  Mika Salo
| Sauber-Petronas
| 1:38,490
| +2,665
|-
! 20
| 16
|  Pedro Diniz
| Sauber-Petronas
| 1:38,576
| +2,751
|-
! 21
| 20
|  Marc Gené
| Minardi-Fondmetal
| 1:39,972
| +4,147
|-
! 22
| 21
|  Gastón Mazzacane
| Minardi-Fondmetal
| 1:40,462
| +4,637
|}

Dirka

|-
! 1
| 3
|  Michael Schumacher
| Ferrari
| 53
| 1:29:53,435
| 1
| 10
|-
! 2
| 1
|  Mika Häkkinen
| McLaren-Mercedes
| 53
| + 1,837 s
| 2
| 6
|-
! 3
| 2
|  David Coulthard
| McLaren-Mercedes
| 53
| + 1:09,914
| 3
| 4
|-
! 4
| 4
|  Rubens Barrichello
| Ferrari
| 53
| + 1:19,191
| 4
| 3
|-
! 5
| 10
|  Jenson Button
| Williams-BMW
| 53
| + 1:25,694
| 5
| 2
|-
! 6
| 22
|  Jacques Villeneuve
| BAR-Honda
| 52
| +1 krog
| 9
| 1
|-
! 7
| 8
|  Johnny Herbert
| Jaguar-Cosworth
| 52
| +1 krog
| 10
|
|-
! 8
| 7
|  Eddie Irvine
| Jaguar-Cosworth
| 52
| +1 krog
| 7
|
|-
! 9
| 23
|  Ricardo Zonta
| BAR-Honda
| 52
| +1 krog
| 18
|
|-
! 10
| 17
|  Mika Salo
| Sauber-Petronas
| 52
| +1 krog
| 19
|
|-
! 11
| 16
|  Pedro Diniz
| Sauber-Petronas
| 52
| +1 krog
| 20
|
|-
! 12
| 18
|  Pedro de la Rosa
| Arrows-Supertec
| 52
| +1 krog
| 13
|
|-
! 13
| 6
|  Jarno Trulli
| Jordan-Mugen-Honda
| 52
| +1 krog
| 15
|
|-
! 14
| 11
|  Giancarlo Fisichella
| Benetton-Playlife
| 52
| +1 krog
| 12
|
|-
! 15
| 21
|  Gastón Mazzacane
| Minardi-Fondmetal
| 51
| +2 kroga
| 22
|
|-
! Ods
| 20
|  Marc Gené
| Minardi-Fondmetal
| 46
| Motor
| 21
|
|-
! Ods
| 9
|  Ralf Schumacher
| Williams-BMW
| 41
| Zavrten
| 6
|
|-
! Ods
| 15
|  Nick Heidfeld
| Prost-Peugeot
| 41
| Vzmetenje
| 16
|
|-
! Ods
| 12
|  Alexander Wurz
| Benetton-Playlife
| 37
| Zavrten
| 11
|
|-
! Ods
| 5
|  Heinz-Harald Frentzen
| Jordan-Mugen-Honda
| 29
| Hidravlika
| 8
|
|-
! Ods
| 14
|  Jean Alesi
| Prost-Peugeot
| 19
| Motor
| 17
|
|-
! Ods
| 19
|  Jos Verstappen
| Arrows-Supertec
| 9
| El. sistem
| 14
|
|}

Opombe
 Michael Schumacher je opravil zadnji postanek v šestih sekundah in za malo ostal pred Häkkinenom. Tako je osvojil svoj tretji naslov prvaka in prvega za Ferrari po Jodyju Scheckterju v sezoni 1979.

Formula 1 sezona 2000
2000

redpajama_set_name: RedPajamaWikipedia


Heroin, manufactured as a trade name by Bayer, was sold along side Aspirin, in chemists.
In France, absinthe became popular having been originally issued to the Army as an anti-malarial remedy, and soon became the drink of choice of artists and poets in Paris, though absinthe in the 1880s was cheap enough to be used by all social classes.
And those who always ate, now eat the more."
Prior to this passage he listed several people of his own acquaintance who, like him, were addicted to the drug, a short list but which ran from lowly poets right up to Secretaries of State. In a country where cholera and dysentery were almost commonplace afflictions and for which laudanum, an opium extract easily available at affordable prices for even the poorest, was considered the most effective treatment (as it was for the common cold too), it makes one wonder just who was ever truly sober at all at any time.
de Quincey was very fair in his assessment both of himself and the drug. He criticised both, but one thing he could say in its favour was that its use had at least helped reduce Londoners' dependency on gin which, at the time of his own birth, had an estimated consumption to dangerous levels by as many as seven in ten of some boroughs' populations.
No one at the time rated beer as a drug at all, and nor did they measure its consumption - given that in many areas it was considered the healthy alternative to water (it had at least been boiled once in its manufacturing process so this was in fact true) and could be manufactured and sold by all manner of unlicensed personnel. All things considered it does rather look like Britain stumbled into the age of empire and industry in a completely alcoholic and drugged-out haze.
I might be approaching my dotage but I'm not that old Trike! ... I distinctly remember being given cocaine (as an injection) when I got a tooth taken out in about 1970 or maybe even a couple of years later. And after the extraction I seem to remember being given a paste (almost certainly cocaine-based) to rub on my gum if it was too painful. I think cocaine was quite routinely administered as a local anesthetic (if one was ever actually given an anesthetic) for dental work until quite a few years later than that.
But it is true, up until about 1914 one could still readily buy laudanum - a refined tinture of opium, as well as arsenic, mercury and cyanide salts, quite simply over the counter at the local high street pharmacy without any doctor's prescription.
Meles, last time I got a tooth removed, I got an injection of Novocaine, must be some form of cocaine derivative.
And gin was just one manifestation of an ancient relationship between Londoners and intoxication. Pepys - no mean drinker himself - was shocked when a "lady", a fellow guest at a mutual friend's house, knocked back a pint and a half of white wine in one swallow. However he wasn't witnessing anything that he hadn't seen his male friends do repeatedly. This habit of drinking wine as if it was beer was noticed also by the Italian diplomat de Mendoza when he arrived in England in 1537 on an unsuccessful mission to arrange a wedding between Henry VIII and Christina of Denmark, the Duchess of Milan. He found Henry perpetually drunk too and seems relieved when his own mission failed and he could get back to Milan and tell the young Duchess what she wasn't missing.
A law designed to obtain revenues from the brewing of stingo (beer flavoured with peppers) passed during the reign of Edward the Confessor made an exemption for private households in that it was the custom to brew the alcohol privately for the benefit of the children's health - children's constitutions not reckoned to be as strong as adults and therefore better suited to downing a few pints of stingo than chancing water or milk. No wonder they developed such a swallowing technique!
Re de Quincey and The Confessions of an Opium Eater, Coleridge and Byron, possibly all the Romantic poets except Wordsworth, indulged in the habit as well.
Of course it wasn't just Britain either over the centuries that was perpetually sozzled, stoned and legless.
The USA was founded by a group of what we would now call "functioning alcoholics" (though even the "functioning" bit might be queried in some cases). At the time of his death George Washington ran one of the largest distilleries in the fledgling nation. Thomas Jefferson as president liked to keep a constant 20,000 bottles of wine in his presidential cellar, and both presidents in their day defeated attempts to address alcohol misuse within the new federation, most notably in Georgia (where rum from the USA's 140 rum distilleries was a problem) and in Virginia (where imported cheap madeira wine mixed with brandy was the poison of preference).
It is well known that the famous Boston Tea Party was a very well lubricated affair, even by the standards of the day. And they were some standards! It has been estimated that the annual per capita guzzle in colonial America just before independence was thirty-four gallons of beer and cider, five gallons of distilled spirits, and one gallon of wine. And the men were as bad!
I'm not surprised by those figures, Nordmann. At roughly the same time, the daily beer ration in the Royal Navy was one gallon per man per day, though this was being replaced by a half-pint of spirits (invariably rum) per man per day. The smaller volume being much easier to store.
But wasn't the navy's daily gallon ration of beer simply each man's general drink ration, since beer kept better than water? And it probably wasn't all that strong, maybe 3% or something like that. That's quite a lot of beer to consume over a day but not really excessive if one's doing hard manual labour. Was the replacement ration of rum issued mixed with water, as grog? I suspect it was and as such surely it was intended to make the stale stored water more palatable, rather than get the crew plastered.
I don't know what the strength of the beer was Meles, I'll take a look at Rogers' Command of the Sea at the weekend.
There is no great evidence that millions of people throughout all this time were dropping like flies from liver disease so there must have been something crucially different between how they imbibed and how we imbibe when dealing with the same quantities. I can't imagine livers in London were in pristine condition during the gin epidemic, but otherwise generations have been swigging back gallons of mead, fortified wine, wine, home-made spirits, stingo, hooch and hundreds of other variations of turpentine in huge quantities without history having recorded mass liver failure at any time.
Given that we are now led to believe that life expectancy wasn't quite so horrendously short as we once thought it was (if childhood was survived of course) and given that liver problems were identifiable and correctly diagnosed since Egyptian times, it does seem strange that the records are rather silent on the matter, doesn't it?
The name historians give to the period in the 50 or so years prior to the First World War, when drug and alcohol usage was commonplace.
Those years also saw raki overtake wine as the favourite drink in the popular 'meyhane' (winehouses) of the Ottoman Empire. It's not clear exactly why this should have been other that that there had been a general liberalisation of the penal code in the empire since 1839. But whatever the specific reason(s) it does seem that the Great Binge was a truly global phenomenon.
That said - and despite the fact that meyhane is a Persian word - neighbouring Persia itself seems to have bucked the trend. Or at least it did so during the Tobacco Protest of the 1890s. This followed a temporary fatwa issued by the Ayatollah Mirza Shirazi against the use of tobacco. Control of the Persian tobacco industry had been about to be conceded by the Shah to British interests but the widely-supported tobacco boycott (prompted by the fatwa) successfully prevented this and forced the Shah to cancel the concession. It was one of the first examples of the potent mix of Shia Islam, nationalism, trade disputes and anti-imperialism which would come to characterise the history of Iran in the following century.
"It is observable and deeply to be lamented that almost every crime except theft originates in drunkenness,and that a large proprtion of the men who are maimed and disabled are reduced to that situation by accidents that happen from the same abominable vice.It is an evil of great magnitude,and one which it will be impossible to prevent so long as the present excessive quantity of spirits is issued in the Royal Navy"
*as it appears in the book.
Vizzer, interesting information regarding the Ottomans and Persia.
During the First World War, Harrods sold a gift set, A Welcome Present for Friends at the Front, which contained cocaine, morphine, syringes and needles.


text:
package edu.utah.further.fqe.impl.service.plan;

import java.util.Collection;
import java.util.Date;
import java.util.List;
import java.util.Map;
import java.util.SortedSet;

import org.apache.commons.lang.builder.EqualsBuilder;
import org.apache.commons.lang.builder.HashCodeBuilder;

import edu.utah.further.core.api.collections.CollectionUtil;
import edu.utah.further.core.query.domain.SearchQuery;
import edu.utah.further.fqe.ds.api.domain.AbstractQueryContext;
import edu.utah.further.fqe.ds.api.domain.IdentityResolutionType;
import edu.utah.further.fqe.ds.api.domain.ImmutableTimeInterval;
import edu.utah.further.fqe.ds.api.domain.QueryAction;
import edu.utah.further.fqe.ds.api.domain.QueryContext;
import edu.utah.further.fqe.ds.api.domain.QueryState;
import edu.utah.further.fqe.ds.api.domain.QueryType;
import edu.utah.further.fqe.ds.api.domain.ResultContext;
import edu.utah.further.fqe.ds.api.domain.StatusMetaData;
import edu.utah.further.fqe.ds.api.domain.plan.Plan;
import edu.utah.further.fqe.ds.api.service.results.ResultType;

/**
 * A decorator of {@link QueryContext}. The only difference between this class and
 * {@link AbstractQueryContext} and sub-classes is the {@link #equals(Object)} and
 * {@link #hashCode()} methods, which determine equality based on the
 * executionId field.
 *

 * {@link QueryPlanPhasedImpl} uses {@link QueryJob} as its dependency graph vertex type.
 *

 * -----------------------------------------------------------------------------------

 * (c) 2008-2013 FURTHeR Project, Health Sciences IT, University of Utah

 * Contact: {@code <further@utah.edu>}

 * Biomedical Informatics, 26 South 2000 East

 * Room 5775 HSEB, Salt Lake City, UT 84112

 * Day Phone: 1-801-581-4080

 * -----------------------------------------------------------------------------------
 *
 * @author Oren E. Livne {@code <oren.livne@utah.edu>}
 * @version Dec 21, 2010
 */
public final class QueryJob implements QueryContext
{
	// ========================= CONSTANTS =================================

	/**
	 * @serial Serializable version identifier.
	 */
	private static final long serialVersionUID = 1L;

	// ========================= FIELDS ====================================

	/**
	 * Delegate.
	 */
	private final QueryContext delegate;

	// ========================= CONSTRUCTORS ==============================

	/**
	 * @param delegate
	 */
	public QueryJob(final QueryContext delegate)
	{
		super();
		this.delegate = delegate;
	}

	// ========================= IMPL: Object ==============================

	/**
	 * @see java.lang.Object#equals(java.lang.Object)
	 */
	@Override
	public final boolean equals(final Object obj)
	{
		if (this == obj)
		{
			return true;
		}
		if (obj == null)
		{
			return false;
		}
		if (getClass() != obj.getClass())
		{
			return false;
		}
		final QueryContext that = (QueryContext) obj;
		return new EqualsBuilder()
				.append(this.getExecutionId(), that.getExecutionId())
				.isEquals();
	}

	/**
	 * @see java.lang.Object#hashCode()
	 */
	@Override
	public int hashCode()
	{
		return new HashCodeBuilder().append(getExecutionId()).toHashCode();
	}

	/**
	 * @see java.lang.Object#toString()
	 */
	@Override
	public final String toString()
	{
		final String executionId = getExecutionId();
		return (executionId == null) ? "broadcast query" : executionId;
	}

	// ========================= IMPL: QueryContext ========================

	/**
	 *
	 * @see edu.utah.further.core.api.state.Switch#start()
	 */
	@Override
	public void start()
	{
		delegate.start();
	}

	/**
	 * @return
	 * @see edu.utah.further.security.api.HasUserIdentifier#getUserId()
	 */
	@Override
	public String getUserId()
	{
		return delegate.getUserId();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getOriginId()
	 */
	@Override
	public Long getOriginId()
	{
		return delegate.getOriginId();
	}

	/**
	 * @param originId
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setOriginId(java.lang.Long)
	 */
	@Override
	public void setOriginId(final Long originId)
	{
		delegate.setOriginId(originId);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.HasNumRecords#getNumRecords()
	 */
	@Override
	public long getNumRecords()
	{
		return delegate.getNumRecords();
	}

	/**
	 *
	 * @see edu.utah.further.fqe.ds.api.domain.QueryActor#queue()
	 */
	@Override
	public void queue()
	{
		delegate.queue();
	}

	/**
	 * @return
	 * @see edu.utah.further.core.api.discrete.HasIdentifier#getId()
	 */
	@Override
	public Long getId()
	{
		return delegate.getId();
	}

	/**
	 *
	 * @see edu.utah.further.core.api.state.Switch#stop()
	 */
	@Override
	public void stop()
	{
		delegate.stop();
	}

	/**
	 * @return
	 * @see edu.utah.further.core.api.state.Switch#isStarted()
	 */
	@Override
	public boolean isStarted()
	{
		return delegate.isStarted();
	}

	/**
	 * @param numRecords
	 * @see edu.utah.further.fqe.ds.api.domain.HasNumRecords#setNumRecords(long)
	 */
	@Override
	public void setNumRecords(final long numRecords)
	{
		delegate.setNumRecords(numRecords);
	}

	/**
	 * @return
	 * @see edu.utah.further.core.api.lang.PubliclyCloneable#copy()
	 */
	@Override
	public QueryContext copy()
	{
		return delegate.copy();
	}

	/**
	 * @return
	 * @see edu.utah.further.core.api.state.Switch#isStopped()
	 */
	@Override
	public boolean isStopped()
	{
		return delegate.isStopped();
	}

	/**
	 *
	 * @see edu.utah.further.fqe.ds.api.domain.QueryActor#fail()
	 */
	@Override
	public void fail()
	{
		delegate.fail();
	}

	/**
	 *
	 * @see edu.utah.further.fqe.ds.api.domain.QueryActor#finish()
	 */
	@Override
	public void finish()
	{
		delegate.finish();
	}

	/**
	 * @return
	 * @see edu.utah.further.core.api.state.StateMachine#getState()
	 */
	@Override
	public QueryState getState()
	{
		return delegate.getState();
	}

	/**
	 * @return
	 * @see edu.utah.further.core.api.state.StateMachine#getActions()
	 */
	@Override
	public SortedSet<QueryAction> getActions()
	{
		return delegate.getActions();
	}

	/**
	 * @param other
	 * @return
	 * @see edu.utah.further.core.api.lang.CopyableFrom#copyFrom(java.lang.Object)
	 */
	@Override
	public QueryContext copyFrom(final QueryContext other)
	{
		return delegate.copyFrom(other);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getExecutionId()
	 */
	@Override
	public String getExecutionId()
	{
		return delegate.getExecutionId();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getDataSourceId()
	 */
	@Override
	public String getDataSourceId()
	{
		return delegate.getDataSourceId();
	}

	/**
	 * @param dataSourceId
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setDataSourceId(java.lang.String)
	 */
	@Override
	public void setDataSourceId(final String dataSourceId)
	{
		delegate.setDataSourceId(dataSourceId);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getQueryType()
	 */
	@Override
	public QueryType getQueryType()
	{
		return delegate.getQueryType();
	}

	/**
	 * @param dataSourceId
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setQueryType(java.lang.String)
	 */
	@Override
	public void setQueryType(final QueryType queryType)
	{
		delegate.setQueryType(queryType);
	}

	/*
	 * (non-Javadoc)
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getIdentityResolutionType()
	 */
	@Override
	public IdentityResolutionType getIdentityResolutionType()
	{
		return delegate.getIdentityResolutionType();
	}

	/*
	 * (non-Javadoc)
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setIdentityResolutionType(edu.utah.further.fqe.ds.api.domain.IdentityResolutionType)
	 */
	@Override
	public void setIdentityResolutionType(final IdentityResolutionType identityResolutionType)
	{
		delegate.setIdentityResolutionType(identityResolutionType);
	}

	/*
	 * (non-Javadoc)
	 * @see java.lang.Object#clone()
	 */
	@Override
	protected Object clone() throws CloneNotSupportedException
	{
		// TODO Auto-generated method stub
		return super.clone();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getParent()
	 */
	@Override
	public QueryContext getParent()
	{
		return delegate.getParent();
	}

	/**
	 * @param parent
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setParent(edu.utah.further.fqe.ds.api.domain.QueryContext)
	 */
	@Override
	public void setParent(final QueryContext parent)
	{
		delegate.setParent(parent);
	}

	/*
	 * (non-Javadoc)
	 *
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getAssociatedResult()
	 */
	@Override
	public QueryContext getAssociatedResult()
	{
		return delegate.getAssociatedResult();
	}

	/*
	 * (non-Javadoc)
	 *
	 * @see
	 * edu.utah.further.fqe.ds.api.domain.QueryContext#setAssociatedResult(edu.utah.further
	 * .fqe.ds.api.domain.QueryContext)
	 */
	@Override
	public void setAssociatedResult(final QueryContext queryContext)
	{
		delegate.setAssociatedResult(queryContext);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getQuery()
	 */
	@Override
	public SearchQuery getQuery()
	{
		return delegate.getQuery();
	}

	/**
	 * @param qid
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getQueryByQid(java.lang.Long)
	 */
	@Override
	public SearchQuery getQueryByQid(final Long qid)
	{
		return delegate.getQueryByQid(qid);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getQueries()
	 */
	@Override
	public List<SearchQuery> getQueries()
	{
		return delegate.getQueries();
	}

	/**
	 * @param searchQuery
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setQuery(edu.utah.further.core.query.domain.SearchQuery)
	 */
	@Override
	public void setQuery(final SearchQuery searchQuery)
	{
		delegate.setQuery(searchQuery);
	}

	/**
	 * @param query
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#addQuery(edu.utah.further.core.query.domain.SearchQuery)
	 */
	@Override
	public void addQuery(final SearchQuery query)
	{
		delegate.addQuery(query);
	}

	/**
	 * @param searchQueries
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setQueries(java.util.Collection)
	 */
	@Override
	public void setQueries(final Collection<? extends SearchQuery> searchQueries)
	{
		delegate.setQueries(searchQueries);
	}

	/**
	 * @param searchQueryToAdd
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#addQueries(java.util.Collection)
	 */
	@Override
	public void addQueries(final Collection<? extends SearchQuery> searchQueryToAdd)
	{
		delegate.addQueries(searchQueryToAdd);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getNumQueries()
	 */
	@Override
	public int getNumQueries()
	{
		return delegate.getNumQueries();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getQueueDate()
	 */
	@Override
	public Date getQueueDate()
	{
		return delegate.getQueueDate();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getQueueTime()
	 */
	@Override
	public Long getQueueTime()
	{
		return delegate.getQueueTime();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getStartDate()
	 */
	@Override
	public Date getStartDate()
	{
		return delegate.getStartDate();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getStartTime()
	 */
	@Override
	public Long getStartTime()
	{
		return delegate.getStartTime();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getStaleDateTime()
	 */
	@Override
	public Date getStaleDateTime()
	{
		return delegate.getStaleDateTime();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#isStale()
	 */
	@Override
	public boolean isStale()
	{
		return delegate.isStale();
	}

	/**
	 *
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setStale()
	 */
	@Override
	public void setStale()
	{
		delegate.setStale();
	}

	/**
	 * @param staleDateTime
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setStaleDateTime(java.util.Date)
	 */
	@Override
	public void setStaleDateTime(final Date staleDateTime)
	{
		delegate.setStaleDateTime(staleDateTime);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getEndDate()
	 */
	@Override
	public Date getEndDate()
	{
		return delegate.getEndDate();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getEndTime()
	 */
	@Override
	public Long getEndTime()
	{
		return delegate.getEndTime();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getTimeInterval()
	 */
	@Override
	public ImmutableTimeInterval getTimeInterval()
	{
		return delegate.getTimeInterval();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getResultContext()
	 */
	@Override
	public ResultContext getResultContext()
	{
		return delegate.getResultContext();
	}

	/**
	 * @param resultContext
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setResultContext(edu.utah.further.fqe.ds.api.domain.ResultContext)
	 */
	@Override
	public void setResultContext(final ResultContext resultContext)
	{
		delegate.setResultContext(resultContext);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getCurrentStatus()
	 */
	@Override
	public StatusMetaData getCurrentStatus()
	{
		return delegate.getCurrentStatus();
	}

	/**
	 * @param currentStatus
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setCurrentStatus(edu.utah.further.fqe.ds.api.domain.StatusMetaData)
	 */
	@Override
	public void setCurrentStatus(final StatusMetaData currentStatus)
	{
		delegate.setCurrentStatus(currentStatus);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getMinRespondingDataSources()
	 */
	@Override
	public int getMinRespondingDataSources()
	{
		return delegate.getMinRespondingDataSources();
	}

	/**
	 * @param minRespondingDataSources
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setMinRespondingDataSources(int)
	 */
	@Override
	public void setMinRespondingDataSources(final int minRespondingDataSources)
	{
		delegate.setMinRespondingDataSources(minRespondingDataSources);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getMaxRespondingDataSources()
	 */
	@Override
	public int getMaxRespondingDataSources()
	{
		return delegate.getMaxRespondingDataSources();
	}

	/**
	 * @param maxRespondingDataSources
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setMaxRespondingDataSources(int)
	 */
	@Override
	public void setMaxRespondingDataSources(final int maxRespondingDataSources)
	{
		delegate.setMaxRespondingDataSources(maxRespondingDataSources);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getStatuses()
	 */
	@Override
	public List<StatusMetaData> getStatuses()
	{
		return delegate.getStatuses();
	}

	/**
	 * @param namespaceId
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setTargetNamespaceId(java.lang.Long)
	 */
	@Override
	public void setTargetNamespaceId(final Long namespaceId)
	{
		delegate.setTargetNamespaceId(namespaceId);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getTargetNamespaceId()
	 */
	@Override
	public Long getTargetNamespaceId()
	{
		return delegate.getTargetNamespaceId();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#isPersistent()
	 */
	@Override
	public boolean isPersistent()
	{
		return delegate.isPersistent();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#isFailed()
	 */
	@Override
	public boolean isFailed()
	{
		return delegate.isFailed();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#isInFinalState()
	 */
	@Override
	public boolean isInFinalState()
	{
		return delegate.isInFinalState();
	}

	/**
	 *
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#clearResultViews()
	 */
	@Override
	public void clearResultViews()
	{
		delegate.clearResultViews();
	}

	/**
	 * @param key
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#removeResultView(edu.utah.further.fqe.ds.api.domain.ResultContextKey)
	 */
	@Override
	public ResultContext removeResultView(final ResultType key)
	{
		return delegate.removeResultView(key);
	}

	/**
	 * @param type
	 * @param intersectionIndex
	 * @param resultContext
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#addResultView(edu.utah.further.fqe.ds.api.service.results.ResultType,
	 *      edu.utah.further.fqe.ds.api.domain.ResultContext)
	 */
	@Override
	public ResultContext addResultView(final ResultType type,
			final ResultContext resultContext)
	{
		return delegate.addResultView(type, resultContext);
	}

	/**
	 * @param other
	 * @see edu.utah.further.fqe.ds.api.domain.HasResultViews#setResultViews(java.util.Map)
	 */
	@Override
	public void setResultViews(
			final Map<ResultType, ? extends ResultContext> other)
	{
		delegate.setResultViews(other);
	}

	/**
	 * @param key
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getResultView(edu.utah.further.fqe.ds.api.domain.ResultContextKey)
	 */
	@Override
	public ResultContext getResultView(final ResultType key)
	{
		return delegate.getResultView(key);
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getResultViews()
	 */
	@Override
	public Map<ResultType, ResultContext> getResultViews()
	{
		return delegate.getResultViews();
	}

	/**
	 * @return
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#getPlan()
	 */
	@Override
	public Plan getPlan()
	{
		return delegate.getPlan();
	}

	/**
	 * @param plan
	 * @see edu.utah.further.fqe.ds.api.domain.QueryContext#setPlan(edu.utah.further.fqe.ds.api.domain.plan.Plan)
	 */
	@Override
	public void setPlan(final Plan plan)
	{
		delegate.setPlan(plan);
	}

	// ========================= METHODS ===================================

	/**
	 * Return the delegate property.
	 *
	 * @return the delegate
	 */
	public QueryContext getDelegate()
	{
		return delegate;
	}

	/**
	 * Evaluate dependency expressions in all {@link SearchQuery}s of this job.
	 */
	public void evaluateDependencyExpressions()
	{
		// Substitute query dependency expression (results from previous queries)
		final List<SearchQuery> queries = CollectionUtil.newList(getQueries());
		for (final SearchQuery query : queries)
		{
			final SearchQueryDeEvaluator evaluator = new SearchQueryDeEvaluator(query);
			evaluator.evaluate();
		}
	}
}

redpajama_set_name: RedPajamaGithub


Magic's April 2010 large set, code-named "Prosper," is called Rise of the Eldrazi.
* Rise of the Eldrazi is part of Zendikar block, which means that it will be part of Zendikar Block Constructed. But it's a large set, which means that it will be drafted on its own; drafts will be either Zendikar-Zendikar-Worldwake or Rise of the Eldrazi-Rise of the Eldrazi-Rise of the Eldrazi.


text:
Galaxy S9 Exynos benchmarks against iPhone X, LG V30 and more
The internet has spent the past 24 hours gushing over the Galaxy S9's subtle (but effective) design changes and an incredibly speedy new camera system, but how does it stack up against other flagships in terms of raw power? That's what Phone Arena attempted to find out on Sunday as it ran the Galaxy S9 with Samsung's own Exynos 9810 SoC up against several other popular devices on a variety of benchmarking apps. The results may surprise you.
Those of you who keep up with Samsung's smartphones will know that the versions with Exynos chips are typically a fair bit faster than the Snapdragon versions. Unfortunately, Samsung only had Exynos phones on hand at MWC 2018, so Phone Arena wasn't able to pit the two against one another. But the data is still interesting.
First up, the Galaxy S9 smoked the competition in AnTuTu, receiving an eye-popping score of 241470. That's around 50% higher than last year's Galaxy S8 and a touch higher than the iPhone X as well. While the LG V30 and OnePlus 5T both had the edge on the Galaxy S8, the S9's score trounces both Android competitors.
The Galaxy S9 couldn't quite match the iPhone X in either the single-core or multi-core Geekbench benchmarks, but dominated all of the 2017 Android flagships. It also overtook its Android rivals (and predecessors) in the GFXBench Manhattan and Car Chase tests, which is good news for Android gamers.
Of course, a vast majority of Samsung fans in the United States will end up with the Snapdragon 845-equipped Galaxy S9, so these tests don't mean all that much for a majority of our readers. But the Qualcomm processor isn't expected to be a major step down, so no matter which version you buy, you're getting one of the fastest Android smartphones on the market when you pick up a Galaxy S9 on March 16th.
Image Source: Zach Epstein, BGR
Tags: galaxy s9, Galaxy S9 Plus, Samsung
Huawei P20 features iPhone X-like notch, dual cameras in new leak
Best Deal We've Found
By Andy Meek 19 hours ago


When it comes to strategy, facilitation, training and development, we offer unique growth drivers! We deliver comprehensive solutions that generate value, which extends way beyond your investment.
We believe that change and growth comes from within each member of the team. We invest in extensive and deep consultation using state of the art survey and assessment tools, which enable us to design programs and solutions around YOUR NEEDS! Participants emerge with real world tangible tools and solutions that address practical issues. In turn, they embrace organisational change.
Our approach, ideas and methodology cut to the chase and enable real decisions about meaningful change and purposeful action. Programs and solutions are delivered by dynamos in touch with the challenges and issues faced by leaders and managers in both the private and development sectors in Africa!
We provide partnership and back up support in the form of follow-up, monitoring tools and feedback workshops. That is the key to real, sustainable development. The result - generating value, which extends way beyond your investment.
Some say we train corporates, others say we empower change and inspire people ... then there are those who view us as team and process integration experts.
The bottom line is that we excel in all three areas and enable our clients to make real breakthrough's and grow! Click on any picture to see what we do.
In 2016 we facilitated the restructuring of the organization for the East African hub and enabled the leadership team to manage more effectively through the design and deployment of a bespoke leadership competency framework.
At the beginning of 2015 we successfully supported our client as it engaged in consolidated growth in the SME – Business Banking sector and finished in the top two banks in the Nielsen ratings in that sector.
In 2013 - 2014, our client invested in Front Line Selling Skills training for all branch staff members across the region. This resulted in product uptake amongst retail customers increasing by 84% in the space of 6 months.
Since the beginning of 2012 the entire organization continues to attend our Empowerment for Service Excellence Program in three countries. This has contributed to Customer Satisfaction Survey levels improving year on year by between 5% and 8% across all banking segments.
Our client empowered its strategy managers who underwent leadership training resulting in 5 out of 7 manager's business units completing projects on time and on budget in the first half of 2013.
In 2013 our client engaged us to facilitate a training solution, to support organizational restructuring and empowerment in two core business units to enable an innovation. Our client has continued to grow its revenues and profitability in these units year on year for the past three years.
Following in-depth consulting and advisory services from us, teams in twenty-four countries underwent change management training in 2012 and 2013. This enabled them to migrate to a new banking technology successfully, on time and on target in 22 out of 24 operations (countries).
We trained a credit card collections team on debt collection and negotiation skills. In 2014 this team's collection performance improved by 48% where as all other teams stayed relatively the same in terms of reducing delinquency.
Ian lives by the company credo empower while you play. He is committed to genuine growth and expects the best from his team and participants in his programs. When attending Ian's programs be ready to be stimulated by his huge energy and ability to engage every participant in a meaningful and memorable way. He's been in the business for over twelve years and is highly respected and valued in areas of change management, team integration, empowerment support, relationship management and strategy facilitation.
Ian has an outstanding ability to see the bigger picture strategically and "connect the dots" practically. He is highly regarded in the telecoms, ICT and financial services sectors as a trainer and consultant who takes you right to where it's all happening be it in operations or at the front line. Ian holds a Bachelor of Commerce and an LLB from WITS University, South Africa. He is a certified DCI Trainer and a Master NLP Practitioner registered with INLP, USA.
Young at heart whilst abundant in knowledge and expertise, he leads various programs at PowerPlay. Participants enjoy his humor and cheerful facilitation style. Watch-out for this man! If you engage him in a discussion on learning and development, prepare for a mind shifting conversation. His core areas of expertise lie in Customer Service Excellence, Front Line Selling and Negotiation Skills. Linus loves technology. Connect with him in most online forums.
Linus has a deep capacity to interact with people in a relaxed and supportive way; whilst still making sure that they emerge from a program ready to deliver new learning's and benefits to their organisation. He is fast becoming a force to be reckoned with in the banking sector. Linus has a degree in Business Information Technology and is a Certified Highlands Ability Battery Facilitator.
Always the Quality Enhancer, Mary-Ann is the observer in the team, looking for ways to improve both communication and processes within PowerPlay and for our clients. Her facilitation style is structured, methodical and inquiring. You may find yourself asking those hard but important questions as you listen to this facilitator. Her steady approach with spontaneous bursts of energy and humour reassures and rejuvenates participants. Don't look for her on social media, she prefers making eye-contact as she communicates with simplicity and sincerity.
Mary-Ann is highly experienced and embodies the name of our consulting arm 'Bridging the Gap' which she formally leads. With her logical, pragmatic way of thinking she wears both the hat of a logistician and a lead facilitator. She brings a presence to any interaction that inspires confidence and professionalism. She holds a bachelors degree in Business Studies, is a certified DCI Trainer and an NLP practitioner and trainer.
Pauline has been a part of the team since 2007. Her passion for customer service excellence ensure that she delivers each and every program with infectious enthusiasm. Her continuous quest to bring out the best in the teams that she trains ensures that every participant leaves the program confident that they will be able to use new techniques when they return to their work place.
Pauline has worked in the HR Development industry for over two decades now, specializing in soft skills training. She also holds an Executive Global MBA from USIU.
I'm passionate about supporting people to achieve their best in the areas they engage. The ability to change and achieve goals lies with an individual, supporting one do so is the ultimate success for me.
Ciarán has a wealth of experience in Executive Coaching, Change Management, Facilitation and Organisational Development. He is also an experienced Executive Coach and is recognised as a world authority in facilitation; having published a book "Facilitation" in 1997.
Ciarán has a highly regarded reputation for dealing with conflict in teams and amongst individuals and as such is engaged at times to work in some very sensitive situations. He is also engaged to facilitate resolution to conflicts across organisations and between organisations both in the public and private sectors.
He has worked in a number of different industries and his client's include the UN,IFAD, The World Food Programme, British Telecom, SAP and Alstom amongst others. He is a visiting lecturer at the University of Limerick, Middlesex and The London Business School.
Ursula specialises in the areas of Change Management, Training and Development, Performance Management, Psychological Assessment and Competency Profiling. With a Masters in Industrial Psychology plus a wealth of HR consulting experience garnered from numerous industries, Ursula brings a special and unique approach to the project teams she works with. This is complimented by extensive work experience in the HR field at the Edcon Group and at Vodacom where she was an Organizational Development Manager and then the Manager for HRD Strategic Interventions.
Rowanne specialises in the areas of Change Management, Business Analysis, Training and Development and Performance Management in Southern Africa with a growing client base in East Africa. She founded RM Consulting in 1995 and has a wide range of HR Consulting experience with significant knowledge of the IT environment. Rowanne has practiced as an Industrial Psychologist for the past 15 years and has experience in a wide range of sectors.
Consistently safeguard and maintain client relationships.
Interpret client behavior more effectively in order to manage relationships and accounts tactically and successfully.
Nurture and fortify client loyalty.
Apply best practice when it comes to attracting and retaining clients.
Understand & anticipate client needs (products, service and price).
Develop the right solutions to ensure retention and continued revenue generation.
Identify sales opportunities with existing clients.
Direct & manage interactions with clients towards sales opportunities.
Communicate new value propositions powerfully and persuasively to prospects and clients.
Perform with an attitude of excellence and a 'solution' focused mindset.
Improve awareness of how teams/ units integrate & connect in the organization.
Empower teams/ units to understand & commit to a common goal.
Appreciate that different teams can view a process in different ways.
See and understand processes in a uniform way.
Develop and align to a common vision for the process.
Enable different teams/ team members to integrate with the process.
Reduce SILO mentality that may hinder delivery within the process.
Rejuvenate commitment and accountability to the process.
Clarify reporting lines & accountability within the context of overall integration.
Reinforce formal and informal channels of communication.
Build and focus on a common sense of purpose – ONE TEAM!
Build trust in each other as we solve challenges together.
Enhance team spirit-UMOJA and rejuvenate productivity levels.
Own the mission and values of the organization and focus on them with a strong sense of common purpose as a team.
Appreciate roles in delivering service excellence to each other and to customers.
Integrate more cohesively by better understanding each other's perspectives.
Understand the power of information and share it optimally.
Embrace and communicate solutions positively and clearly.
Get to know each other better, have fun and bond.
Thriving managers who inspire themselves from within to cut through every challenge like a hot knife through butter.
Managers who exude positive energy as they motivate others.
Appreciate the real meaning and purpose of Mentoring.
Differentiate Mentoring from Coaching and Training.
Understand the role of an effective Mentor.
Learn how to use a range of Mentoring tools to mentor effectively.
Identify common skills in the Mentor-Mentee relationship.
Get to know the key stages in the development of a Mentor-Mentee relationship and what to do in each stage.
Face the challenges of being a committed Mentor in today's business environment.
Inspire and positively influence team members.
Gain cooperation at every level of the organization.
Lead a winning high performance team or department.
Empower others to deliver results consistently.
Recognize individual and team success.
Give constructive feedback when reviewing & managing performance.
Manage employee performance and development for continuous improvement.
Plan effectively for negotiation scenarios.
Understand needs/ interests so as to communicate value propositions.
Move towards agreements for mutual gain.
Manage/ cushion objections, aggressive comments and threats.
Maintain a "thick skin" and not take aggressive comments personally.
Manage comparisons and tricky questions in negotiation scenarios.
Get "solution" focused in our thinking and speaking as we negotiate.
Explain clearly & secure understanding so that all are on the same page.
Remain focused on the outcome and not get personal/ emotional.
Respond tactfully and tactically to tricks/ evasive ploys.
Be assertive without being threatening.
Build confidence when dealing with challenging customers/ parties.
Secure commitment to agreements – what is agreed is actioned on the ground.
Appreciate that performance management is underpinned by the ability to enable.
Grip the essential elements of Enabling Performance Management (EPM) in the organization.
Understand "influencing situations" and sources of power.
Develop and apply the people functions of an effective manager.
Master the skill of influencing direct reports/ team members.
Understand behavioral profiles and how to manage resistance.
Acquire enhanced listening and reflection skills.
Identify and set goals in a clear and structured way.
Practice the giving of constructive and positive feedback.
Appreciate what sources of power to use when influencing employees.
Appreciate that everyone at the front line has the opportunity to cross sell.
Sharpen ability and alertness to identify needs of customers for other products; and progress these into opportunities.
Direct customer interactions towards sales opportunities.
Strike the balance between customer satisfaction & driving sales of new products.
Use CRM systems to source and manage information.
Appreciate key elements/ steps of a sales process within the practical context of front line selling.
Understand psychological motivators which influence a buy decision.
Communicate value propositions & present solutions persuasively.
Handle objections/ comparisons as we communicate value propositions.
Master & apply specific "closing the sale" techniques.
Promote employee passion and commitment to a customer-centric organization.
Crystallize the meaning of "great service" amongst all employees. Every employee adopts the approach that "every customer is my customer".
Enhance understanding of customers' needs & wants, so that the company is seen as a responsive partner that delivers real solutions.
Understand internal & external customer needs, so that everyone excels together in service excellence and professionalism.
Consistently excel at the way one communicates with customers & team members.
Improve commitment to service standards and relevant global best practice.
Reduce SILOS and generate cohesion between back office operations and frontline teams resulting in consistent & efficient delivery of quality service.
"Own" the main goal and passionately go beyond one's role to support the team in its overall quest to deliver solutions to customers.
See feedback as an opportunity to correct & improve so that the company develops superb delivery and recovery systems.
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Talley Student Union at NC State University | Providing spaces and venues for the Wolfpack community that encourage collaboration, foster learning and provide a welcoming atmosphere.
Students can make a difference on campus with assistance from one of the nine departments from Student Leadership & Engagement and the Office of Institutional Equity & Diversity, which will be housed in Talley and anchored by the involvement center. The James H. Woodward Student Involvement Center supports more than 600 student organizations — including Student Government and the Union Activities Board — and serves as a critical starting point for creating a meaningful co-curricular experience at NC State. Talley Student Union is also home to the student governance activities such as the Student Centers Board of Directors, which ensures that both the facilities and the social and cultural programs are operated in students' best interest.
Students are also present in Talley Student Union as part of a vibrant student employment program preparing students with transferable skills that help them in the classroom and the workplace. These student employment opportunities include management and technical positions as well as support roles. Campus Enterprises currently employs more than 500 NC State students, and 50 of those students are employed in one of four student centers. This number will grow as Talley expands; hundreds of employees and student leaders from various departments and divisions, will volunteer in and work to manage the facility when construction is complete. Through these employment opportunities are a concrete way students can enhance their learning, improve their success, and provide ownership of the Talley Student Union.
Share your Talley Student Union experience! Simply add the hashtag #talleystudentunion and have your photo featured on this site!
Core to the vision of this facility is to be sustainable and environmentally friendly. We are not only committed to sustainability but also to advancing sustainability on campus. Our society and students demand responsible stewardship of our environmental resources, and our goal in the Talley Student Union is to be a leader in sustainable initiatives.
Not only is the Talley Student Union expected to be LEED Silver Certified with a variety of building features to accomplish this goal, but the operations within the building also raise the bar. From composting to green cleaning, you can expect to find a variety of ways we are helping the university achieve the vision of sustainability.
Talley Student Union is managed by Campus Enterprises; NC State's lead division for retail and hospitality.


Ubuntu live PXE how to save settings?
It gets me to a GUI and lets me install Ubuntu just as installing from the CD would.
I want to know if I can install some software and change some settings and then save these changes so every time I fire up the ubunutu 10.10 PXE I can go back to this settings.
Nice, I've been planning to try a Ubuntu based PXE boot. For Live USB persistence a loop file called casper-rw is used, perhaps you can do the same with PXE. Put the word persistent after boot=casper on a menu entry. You can also use a partition called casper-rw instead of the loop file. Hey I bet you can nfs mount the casper-rw partiton.


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Home > About Us > News > Physician Services to transition to UNC Physician Network next summer
Physician Services to transition to UNC Physician Network next summer
UNC Health Southeastern's Physician Services division will transition to UNC Physician Network in July 2022.
The overall health system entered a management services agreement with UNC Health in January, making it a part of North Carolina's leading public academic health system.
"This is the next step in the process as we further align with the UNC Health system," said UNC Health Southeastern President/CEO Joann Anderson. "We are excited to collaborate with UNCPN to provide exceptional Carolina Care to our patients, including new and expanded access to the expertise and specialties and other benefits of a leading health system."
"UNC Physicians Network is excited to partner with UNC Health Southeastern and the tremendously dedicated, hardworking and talented teams serving Robeson County," said Lynne Fiscus, MD, MPH, President/CEO of UNC Physicians Network. "It has been a pleasure getting to know them, and we look forward to working together and focusing on health outcomes and health equity in the community."
UNC Health Southeastern Physician Services includes primary, urgent care, and specialty clinics, which will still have autonomy to operate and make decisions. As a physician-led network, UNCPN will provide opportunities for clinicians to participate in network governance and leadership.
"It is clear that UNC Physicians Network and UNC Health Southeastern share similar visions in providing safe, high-quality and equitable patient care," said Sachin Gupta, MD, Chief Medical Officer of UNC Physicians Network. "We remain committed to keeping our decisions patient-centered and clinician-driven, and see great things coming from this partnership."
UNC Health Southeastern Vice President and Interim Chief Operating Officer Lori Dove added, "UNCPN values our organization and the care we provide in our community and are working closely with them to ensure it is smooth transition."
This transition does not include Southeastern Cardiology and Cardiovascular Clinic, Southeastern Wound Healing Center, or Southeastern Radiology Associates as these clinics fall under a different management structure. Other UNC Health Southeastern affiliates, such as Gibson Cancer Center and The Surgery Center, are also not part of this network transition.
UNC Health Southeastern will also be transitioning from its current Epic electronic medical record system to UNC's Epic system in July 2022.
Joann Anderson, President/CEO
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