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International Journal of Cardiology /
Ramasamy, A. (Anantharaman), Chen, Y. (Yang), T. Zanchin (Thomas), Jones, D.A. (Daniel A.), Rathod, K. (Krishnaraj), Jin, C. (Chongying), Y. Onuma (Yoshinobu), Zhang, Y.-J. (Yao-Jun), Amersey, R. (Rajiv), Westwood, M. (Mark), et al. M. Ozkor (Muhiddin), O'Mahony, C. (Constantinos), A.J. Lansky (Alexandra), T. Crake (Tom), P.W.J.C. Serruys (Patrick), A. Mathur (Anthony), A. Baumbach (Andreas) and C.V. Bourantas (Christos)
Optical coherence tomography enables more accurate detection of functionally significant intermediate non-left main coronary artery stenoses than intravascular ultrasound: A meta-analysis of 6919 patients and 7537 lesions
International Journal of Cardiology
Objective: Fractional flow reserve (FFR) is regarded as the gold standard for the physiological assessment of intermediate coronary artery stenoses. However, FFR does not allow assessment of plaque morphology and lesion geometry. Intracoronary imaging techniques such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT) can help treatment planning by optimising stent implantation, which can improve patient outcomes. The aim of this meta-analysis is to compare the efficacy of IVUS and OCT-derived metrics in detecting flow limiting stenoses in non-left main stem lesions. Methods: A systematic review of PubMed, Medline, and Cochrane databases was performed and identified studies examining the diagnostic accuracy of IVUS and OCT in detecting significant stenoses when compared to FFR. Results: A total of 33 (7537 lesions) studies (24 IVUS, 7 OCT and 2 IVUS & OCT studies) were included in the meta-analysis. Pooled analysis showed that IVUS- and OCT-derived minimum lumen area (MLA) had a similar sensitivity in predicting haemodynamically significant lesions (IVUS-MLA: 0.747 vs OCT-MLA 0.732, p = 0.519). However, OCT-MLA had a higher specificity (0.763 vs 0.665, p < 0.001) and diagnostic accuracy in detecting flow-limiting stenoses than IVUS-MLA (AUC 0.810 vs 0.754, p = 0.045). Sub-analysis of the studies with the clinically significant FFR cut-off value of 0.80 yielded similar results demonstrating that OCT-MLA has a better accuracy than IVUS-MLA in detecting haemodynamically significant stenoses (AUC 0.809 vs 0.750, p = 0.034). Conclusions: OCT with its superior image resolution appears to be the preferable intravascular imaging modality for the detection of haemodynamically significant stenoses in non-left main stem lesions.
Keywords Fractional flow reserve, Intravascular ultrasound, Optical coherence tomography
Persistent URL dx.doi.org/10.1016/j.ijcard.2019.09.067, hdl.handle.net/1765/120966
Journal International Journal of Cardiology
Ramasamy, A. (Anantharaman), Chen, Y. (Yang), Zanchin, T, Jones, D.A. (Daniel A.), Rathod, K. (Krishnaraj), Jin, C. (Chongying), … Bourantas, C.V. (2019). Optical coherence tomography enables more accurate detection of functionally significant intermediate non-left main coronary artery stenoses than intravascular ultrasound: A meta-analysis of 6919 patients and 7537 lesions. International Journal of Cardiology. doi:10.1016/j.ijcard.2019.09.067


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Reading: Words, clauses, sentences, and T-units in learner language: Precise and objective units of m...
Words, clauses, sentences, and T-units in learner language: Precise and objective units of measure?
Taina Mylläri
Department of Language and Communication Studies, University of Jyväskylä, FI
In research on learner language complexity, accuracy and fluency (CAF), syntactic complexity is often studied with quantitative measures based on words, clauses, sentences, and T-units. The findings have been mixed, but segmenting learner language into these units of measure has seldom been problematised, even if the need for accurate coding is well known. The present study explores words, clauses, sentences, and T-units as production units in written learner language using a corpus of 352 L2 Finnish texts (28,813 words). The results illustrate how written learner language can be hard to fit into the production unit categories, which are essential for the most frequently used quantitative measures of syntactic complexity. On the one hand, the results support calls to include explicit definitions of the units of measure when reporting findings obtained with these quantitative measures. On the other hand, they align with calls to introduce new measures to better gauge the changes in learner language syntax as it develops with increasing language proficiency.
Keywords: Common reference levels, Complexity, Learner Finnish, Learner writing, Segmentation
How to Cite: Mylläri, T. (2020). Words, clauses, sentences, and T-units in learner language: Precise and objective units of measure?. Journal of the European Second Language Association, 4(1), 13–23. DOI: http://doi.org/10.22599/jesla.63
Accepted on 20 Jul 2020 Submitted on 22 Feb 2020
When second-language (L2) learning is analysed in terms of complexity, accuracy, and fluency, complexity is often quantified using measures that are based on the length of clauses, sentences, and T-units, or on the relation of these production units to each other (e.g., Bulté & Housen, 2012; Pallotti, 2015; Wolfe-Quintero et al., 1998). These measures require the consistent and reliable segmenting of learner language, but the possible effects of inconsistencies in coding learner language have seldom been discussed (e.g., Byrnes et al., 2010, p. 169).
Learner language does not always fit neatly into the categories used in these quantitative measures of complexity. Deviations from the target language norms are a challenge for annotation (e.g., Granger, 2002), and there can be several interpretations of the intended target form (e.g., Brunni et al., 2015; Ragheb & Dickinson, 2011; Rehbein et al., 2012). These challenges affect the segmenting of learner language into clauses, sentences, and T-units, especially on lower proficiency levels, when learner language can be fragmented and elliptic in both its oral (e.g., Foster et al., 2000) and written forms (e.g., Martin, 2013). The ambiguity of clause and sentence boundaries in written learner language is illustrated by Martin's (2013) segmenting experiment, in which a group of 35 university students of Finnish segmented three learner Finnish texts into clauses and sentences. The results showed variation in the numbers of both sentences and clauses, and even when two students arrived at the same number of clauses or sentences, the production units identified were not necessarily identical (Martin, 2013).
Differences in the numbers of production units are likely to lead to different results when complexity is measured using these units. Segmenting learner language into clauses, sentences, and T-units may also affect the quantitative measures that have typically been used to measure the syntactic complexity of written learner language, as among the most frequently used measures have been mean length of sentence, mean length of clause, mean length of T-unit, mean number of clauses per T-unit, mean number of T-units per sentence, and mean number of dependent clauses per clause (e.g., Ortega, 2003).
The present study seeks to explore how objective and reliable words, sentences, clauses, and T-units are as units of measure in written learner language. This is done by taking a close look at the segments that cause difficulties in splitting the data into these production units. The research question is: How do deviations from target language norms affect the segmenting of written learner language into words, sentences, clauses, and T-units? To answer this question, a corpus of written learner Finnish texts from different proficiency levels, from beginners to advanced, was segmented into these production units, and the segments not fitting into these categories were analysed. While the results are in part language specific, the problems are not limited to learner Finnish: Similar problems arise with other languages too.
2. Word, sentence, clause, and T-unit as production units
When words, clauses, sentences, and T-units are used as units of measure, they need to be identified in the data and their frequency of occurrence needs to be counted. These units can, however, be defined in more ways than one. In this section, words, clauses, sentences, and T-units are discussed in relation to their use in measuring syntactic complexity.
2.1. Word
One way to measure complexity is to calculate the mean length of a given production unit in words (e.g., Bulté & Housen, 2012). In many languages, a word can be defined as an orthographic unit separated from other text units by a blank space or by punctuation. While this simple definition is not suitable for all languages and it may overlook some linguistic features of words and differences between languages (e.g., Booij, 2012), it can in many cases be considered a reasonable way of defining a word in written language (Haspelmath, 2011, p. 69). It also makes automated word counts easy in languages in which words are separated by blank spaces.
This simple definition of a word seems reasonable within a study or within a language, but some language-specific conventions or orthographic rules, such as those concerning compound words, may cause differences in word count. When the number of words is based on orthography, elements in compound words are each counted as one word if they are separated from other elements by a blank space. This way of counting seems suitable for the present study, as compound words in Finnish normally consist of two or more words spelled as one orthographic unit (e.g., ruokapöytä for ruoka+pöytä 'food' + 'table') 'dining/dinner table'. It may, however, cause problems in languages with different orthographic conventions. Additionally, errors in orthography with compound words made by both L2 and first-language (L1) writers, such as iso äiti for isoäiti 'grandmother' or jokapäivä for joka päivä 'every day', may affect the word count.
Another possible source of differences in the length of a clause, sentence, or T-unit in words are differences in morphology. In morphologically rich languages, some syntactic information may be encoded within a single word, as illustrated in example (1). Such differences, and their impact on word count, should be taken into consideration if the length of a given syntactic unit in words is compared across languages.
(1) talo-ssa=ni luk-isi-t=ko
house-INESS=POSS.1SG read-COND.2SG=Q
'in my house' 'would you read'
Some less-frequently occurring elements in written texts may also affect the word count. These include abbreviations pointing to multiple words (e.g, jne for ja niin edelleen 'and so on'), orthographic units containing hyphens or slashes, and word-like units containing or consisting of other characters than letters of the alphabet, such as expressions of quantity written with numbers (e.g., 1–2), or amounts specified with a combination of a number and a unit of measurement (e.g., 12 tuntia '12 hours'; 11 tuntia '11 hours').1
2.2. Clause
Some of the most widely used measures of syntactic complexity involve counting the number of clauses per given unit (Pallotti, 2015) and mean length of clause in words (e.g., Ortega, 2003). Although grammars offer relatively clear definitions of a clause, in reality texts, both in L1 and L2, contain segments that do not fit these descriptions. Nevertheless, these segments should also somehow be acknowledged and included in analyses of complexity.
In studies on syntactic complexity in learner language, especially in learner English, a clause has typically been defined as a production unit containing either a subject and a finite verb or a subject and a finite or non-finite verb form (e.g., Lu, 2011, p. 44; Wolfe-Quintero et al., 1998, p. 70). When measuring syntactic complexity, infinitive forms in verb clusters can be considered to either belong to a verb construction within one clause or to form non-finite dependent clauses (e.g., Pallotti, 2015). In Finnish, structures with a non-finite verb form are typically considered verb phrases rather than clauses (Hakulinen et al., 2004, pp. 488–489; Vilkuna, 2003, pp. 14–15). Regarding the measures of complexity, coding verb clusters to belong to one clause or to more clauses has an impact on the mean length of clause, as well as on the number of clauses (Bulté & Housen, 2012). This decision also affects the number of dependent clauses and thus any ratios in which the number of dependent clauses is used.
In the above definitions of a clause, a subject is also considered a mandatory element. While this requirement suits non-null-subject languages, such as English, it is not practical for null-subject languages or partial null-subject languages, such as Finnish. In a quantitative study of Finnish syntax, Hakulinen et al. (1996) conclude that an overt subject cannot be considered a mandatory element of a clause in Finnish, because in their data, consisting of factual prose such as newspaper articles, more than 30% of the clauses did not have an overt subject (Hakulinen & Karlsson, 1980). There are several linguistic features contributing to this. In Finnish, it is possible to incorporate the first- and second-person subject in the verb form, leaving out the corresponding pronoun. Hence, for example, 'I say' can be expressed either with two words (minä sanon) or one word (sanon). There are also clause types that do not allow an overt subject. These types include all clauses in the passive voice (Hakulinen et al., 2004, p. 1245; Karlsson, 2015, p. 200) and some clauses containing meteorological expressions (e.g., Satoi. rain-PAST-3SG 'It was raining.') or causative verbs (e.g., Minua pelottaa. me-OBJ frighten-PRS-3SG 'I feel frightened.') (e.g., Karlsson, 2015, p. 81; for more detail, see Hakulinen et al., 2004, pp. 856–862, 1286). Such differences between languages need to be considered when defining a clause.
2.3. Sentence
In segmenting written language, the sentence can be considered "the obvious unit" (Ellis & Barkhuizen, 2005, p. 147). A sentence is usually defined as an orthographic unit beginning with a capital letter and ending with appropriate punctuation. These indicators of sentence boundaries are marked by the writer, but in some texts, the use of punctuation and capital letters may be inconsistent. These inconsistencies may be caused by problems in writing in the target language or by problems in writing in general.
The unsystematic use of punctuation can sometimes create sentences without a verb (as in example (2)) or an apparent independent clause (see example (3)). Considering this kind of punctuation intentional or erroneous affects the number of sentences and the kind of elements they consist of.
purukumia
chewing.gum
tunnilla
in.class
ellei
unless.not
häiritse.
disturbs
muita.
'You/One can eat chewing gum in class unless it disturbs. others.' (F-010, adolescent A1)
Oppilaat
eivät
ottaa
kännyköitä
kouluun
to.school
mukaan.
häiritse
tunneilla.
in.classes
'Pupils should not take mobile phones to school. Because they disturb the class.' (F-733, adolescent B1)
Not all sentences without a verb or an independent clause result from errors in punctuation. For example, newspaper headlines, interactive elements such as greetings, and certain idiomatic expressions can be punctuated as sentences even when they do not contain a grammatically complete clause (e.g., Biber et al., 1999, pp. 224–225; Leech & Svartvik, 2002, p. 262). This also applies to Finnish. According to standard Finnish grammar, the minimal length of a sentence is one word, and this word does not need to be a verb (Hakulinen et al., 2004, p. 827).
There are also sentences that contain only clauses or structures that are traditionally not considered independent. For example, Foster et al. (2000) raise the question of the dependence or independence of adverbial clauses beginning with the conjunction because but lacking an apparent main clause. In written Finnish, sentences containing only clauses that begin with a subordinator can be found in both L1 and L2 writers' texts (Kalliokoski, 2006). In Finnish, there are also sentences that contain only infinitive verb forms (Visapää, 2008).
Sentences containing grammatically incomplete clauses or lacking an independent clause present a challenge to coding learner language and to the quantitative measures of complexity. Annotating these sentences to contain at least one clause or zero clauses affects all measures in which the number of clauses is used. Similarly, coding these sentences to contain at least one independent clause or only dependent clauses also affects measures relying on the number of dependent or independent clauses.
2.4. T-unit
The T-unit, first introduced by Hunt in 1965 in the L1 context, has gained ground in L2 research, but it has also been the target of some criticism (Bardovi-Harlig, 1992; Biber et al., 2011; Crossley & McNamara, 2014). There are several definitions of the T-unit. Most often it refers to one independent clause and any dependent clauses attached to it, although there has been variation in the inclusion or exclusion of fragments and in the counting of elements across sentence boundaries (e.g., Foster et al., 2000, pp. 360–363). In measuring syntactic complexity, the T-unit is among the most popular production units (Foster et al., 2000; Ortega, 2003; Wolfe-Quintero et al., 1998).
However, the relationship between clauses can sometimes be ambiguous, which makes it hard to determine whether a clause is coordinated or subordinated (Lieko, 1992, pp. 29–31; Quirk et al., 1972, pp. 795–796). Additionally, it is not always clear which independent clause is the main clause of a given dependent clause (as in example (4)), where it is not clear which of the independent clauses functions as the main clause for the clause beginning with jos 'if'.
at.home
kiire,
valmistan
I.make
ruokaa,
huomasin
I.noticed
että
maitoa,
I.go
lähikauppaan.
to.corner.shop
'if it's busy at home, I cook, and I noticed that there is no milk, I go to the corner shop.' (F-253, adult A2)
Nevertheless, distinguishing between the two and identifying the dependency relationships are essential when using the T-unit as a unit of measure.
3. Design of the study
In the present study, a corpus of written learner Finnish and a comparative set of L1 Finnish adolescent writers' texts were split into words, sentences, clauses, and T-units to create a corpus for measuring syntactic complexity in learner Finnish with the frequently used quantitative measures. To find the production units, a set of definitions, described in Section 4, was used, and segments not fitting into these categories were examined. The focus was on problematic segments that could lead to different interpretations of the number of the relevant production units (i.e., words, sentences, independent clauses, and dependent clauses). The problematic segments were analysed qualitatively and quantified by counting their frequency. The aim was to identify the key challenges and evaluate their significance.
3.1. The data
The data in the present study comprise 352 learner Finnish (L2) texts (28,813 words) and 128 native Finnish (L1) texts (7,049 words) from the Cefling project corpus,2 which contains texts elicited by means of communicative writing tasks. The Cefling corpus was collected for L2 research by selecting L2 Finnish adult learner texts from the National Certificates of Language Proficiency examination database and by collecting texts from adolescent L2 Finnish learners and L1 writers in school years 7 to 9 (age 12 to 16) with matching tasks (Martin et al., 2010). For the present study, the argumentative texts from the Cefling corpus were used.
To facilitate research into the development of different linguistic features in relation to language proficiency, all the L2 Finnish texts were assessed and placed according to the proficiency levels of the Common European Framework of Reference (CEFR, Council of Europe, 2001) by a team of trained raters in the Cefling project. Each text was rated by three raters using scales based on the CEFR (Alanen et al., 2010). The reliability of the ratings has been shown by both quantitative and qualitative analysis (for more detail, see Huhta et al., 2014). The adult learners' texts cover CEFR proficiency levels A1 to C2, and the adolescent learners' argumentative texts cover levels A1 to B1.
In the present study, segments that were copied word by word from the task prompts or contained only verbless greetings, pseudonyms, or contact information were considered echo responses and interactional elements, and they were not included in the analysis (cf. Foster et al., 2000). This led to the exclusion of 328 segments (961 words). The remaining text in the Cefling project Microsoft Word files was organised into a project corpus (Table 1).
Amount and distribution of data across different writer groups.
CEFR level/school year
A1 50 2,261 32 775 – – 82 3,036
A2 37 2,272 39 1,589 – – 76 3,861
B1 43 5,142 40 2,232 – – 83 7,374
B2 35 4,166 – – – – 35 4,166
C1 46 5,876 – – – – 46 5,876
Year 7 – – – – 55 2,902 55 2,902
Year 9 – – – – 23 976 23 976
Total 241 23,596 111 4,596 128 6,709 480 34,901
To enable comparisons between language learners and native speakers, the L2 and L1 data were kept separate. To observe differences between learner age groups and between proficiency levels, the L2 data were separated into two groups, referred to in this study as adult learners and adolescent learners, and arranged according to the assessed proficiency level. Similarly, the L1 data were organised into three subgroups based on the school year of the participants.3
3.2. Analysis of the data
To answer the research question, the data were coded as words, sentences, clauses, and T-units. Segments not complying with the definitions and thus not fitting into these categories were analysed linguistically, and the frequency of such segments was calculated. On the sentence level, the focus was on irregularities in sentence marking which could affect the number of clauses, sentences, and T-units. On the clause level, the focus was on segments that could affect the number of clauses or their status as independent or dependent. If the problematic segments were not considered to affect the number of production units or the division of clauses into independent and dependent, they were outside the scope of this study.
Because there was only one annotator and a high number of problematic segments were found during coding, the sentence-level segmentation was compared with two other segmentations of the same data. The segmentation in the Cefling project CHAT files was one of those used. During the Cefling project, the texts were divided into sentences by seven native Finnish-speaking graduate students pursuing their Master's degree in Finnish language. If a sentence could not be clearly identified, the students were instructed to divide the text into clauses or, if the clause boundaries were also ambiguous, to group the text into clauses around the finite verbs (Cefling project, unpublished instructions). In the Cefling project, problematic segments were discussed but no inter-annotator agreement was counted or reported. The second segmentation used the open-source dependency parsing pipeline for Finnish developed by the University of Turku natural language processing (NLP) group.4 The Finnish Dependency Parser is a statistical parser based on open-source NLP tools and trained on the Turku Dependency Treebank, whose system of annotation is a Finnish-specific adaptation of the Stanford Dependency scheme (Haverinen et al., 2014).
To evaluate the reliability of the sentence-level segmentation, the three segmentations were compared using precision, recall, and F-score, which is the harmonic mean of the two. None of the segmentations was used as a gold standard annotation but instead, precision and recall were counted following Lu (2010) and Brants (2000) by dividing the number of segments identical in both the compared sets by the total number of sentences in the first set (precision) and in the second set (recall). In this kind of comparison setup, precision, recall, and F-score are considered to reflect agreement between annotations, the F-score being considered the most informative of the three (Brants, 2000; Lu, 2010).
4.1. Words
In the present study, a word was defined as an orthographic unit containing alpha-numeric characters and separated from other units by a blank space, punctuation, or other orthographic marker, such as the beginning or the end of a line or a paragraph.
During the sentence-level comparisons, the orthography of each word in the two manual segmentations was checked and aligned to eliminate inconsistencies due to typing errors or differences in typing conventions between the file formats. Any discrepancies were resolved, when possible, based on the hand-written originals (adolescent learners and L1 writers) or the original database files (adult learners), and otherwise based on the transcription in the Word files. This resulted in identical word counts in the two manual segmentations.
In the automatically segmented data, there were four words more in the adult learner data and two words more in the L1 data than in the manual segmentations. The differences were caused by non-alphabetic characters within a word, such as quotation marks or a colon connecting a letter and a case ending. There were no differences in the word count in the adolescent learner data.
4.2. Sentences
A sentence was initially defined as an orthographic unit beginning with a capital letter and ending with a full stop, question mark, exclamation mark, or any combination of these. However, the requirement of initial capitalisation was discarded during segmenting because in some texts all the writing was originally in block capitals, or random block capitals were used within words. Consequently, segments such as those in example (2) were also coded to contain two sentences. The requirement of punctuation at the end of a sentence was also re-evaluated, and other orthographic markers, for example the organisation of text into items on a bulleted or numbered list, were considered to be indicators of sentence boundaries, as some texts were partly or completely organised as lists (as in example (5)).
(5) Minä olin syömässä ravintolassa Helsingissa, minä nähnyt 3 huonoa asiaa ja 1 hyvä asia
1/- ruokaa on hyvää.
2/- paljon ihmiset, ei riita paikkalla,
3/- He puhuvat kovaa
4/- ravintolassa tosi kuuma.
'I was eating at a restaurant in Helsinki, I seen 3 bad things and 1 good thing
1/- food is good.
2/- a lot of people, no quarrel at place,
3/- They speak loudly
4/- at the restaurant really hot.' (F-1012, adult A1)
In example (5), which is a short text from the lowest proficiency level, there is only one sentence indicated with both initial capitalisation and punctuation at the end. After careful consideration of such cases, the working definition of a sentence was changed, and the end of a whole text, a text paragraph, or a list item in a bulleted or numbered list were also defined as ending a sentence, regardless of the punctuation.
To evaluate the effect of the changes in the definition of a sentence, the sentence-level segmentation was compared to the original definition, and sentences not falling within the original definition were divided into two categories: Those ending with standard punctuation but not beginning with a capital letter, and those having no standard punctuation at the end (Table 2). The comparison showed that with proficiency level A1, only around half of the sentences conformed to the original definition of a sentence. Inconsistencies in punctuation were more frequent in the learner texts than in the L1 texts, where they were rare. These results should not, however, be interpreted as a straightforward relationship between the use of punctuation and L2 proficiency, as the inconsistent use of punctuation may have been caused by difficulties in writing in general, not necessarily difficulties in writing in a L2.
Number and percentage of sentences with initial capitalisation and standard punctuation, sentences ending with standard punctuation but lacking initial capitalisation, and sentences not ending with standard punctuation.
Writer group
Initial capital and standard punctuation
Standard punctuation but no initial capital
No standard punctuation
Total sentences
Adult A1 154 48 60 19 108 34 322
Adult A2 234 63 29 8 109 29 372
Adult B1 414 88 30 6 25 5 469
Adult B2 378 95 6 2 12 3 396
Adult C1 537 98 4 1 9 2 550
Adult C2 367 96 3 1 12 3 382
Adolescent A1 48 55 15 17 24 28 87
Adolescent A2 118 86 13 9 6 4 137
Adolescent B1 182 93 8 4 5 3 195
L1 year 7 268 93 8 3 11 4 287
L1 year 8 254 92 10 4 12 4 276
L1 year 9 104 94 2 2 5 5 111
As for the actual number of sentences, there were only small differences in the numbers found in the different segmentations, and agreement between the segmentations was high, 90% to 99%, except in the adolescent learner data, where it was 85% and 88% on levels A1 and A2 in the comparison of the two manual segmentations (Table 3). The high agreement indicates that the sentences found were mainly identical.
Number of sentences in each segmentation and the results of the sentence-level comparisons.
Number of sentences in different segmentations
Number of identical sentences
Agreement between segmentations (F-score)
Present study
CHAT files
Parsed texts
Present study and CHAT files
Present study and parsed texts
Present study vs. CHAT files
Present study vs. parsed texts
Adult A1 322 337 308 308 296 0.93 0.94
Adult B1 469 488 450 452 430 0.94 0.94
Adult C1 550 554 545 546 530 0.99 0.97
Adolescent A1 87 97 84 78 81 0.85 0.95
Adolescent A2 137 145 131 124 123 0.88 0.92
Adolescent B1 195 201 192 185 183 0.93 0.95
L1 year 7 287 290 280 285 259 0.99 0.91
The Cefling project segmentation contains the highest number of sentences in all the writer groups, which is in line with the instructions to split the text into clauses if the sentence boundaries were unclear. The parsed texts were found to contain the smallest number of sentences in all the writer groups. According to Haverinen et al. (2014), the parser makes its decisions based on dependencies and does not follow any separately given rules for sentence splitting.
These results seem to suggest that the working definition used in the present study could provide reliable enough criteria for identifying a sentence. It seems that the absence of an initial capital letter can be ignored. Further, the end of a list item in a bulleted or numbered list, the end of a text paragraph and the end of the whole text could be considered indicators of a sentence ending, even if none of these markers are included in the standard definition of a sentence.
4.3. Clauses
A clause was defined as a segment within a sentence containing a finite verb and all its arguments and adjuncts. As Finnish is considered a partial null-subject language, a subject was not required. Following the definition in Hakulinen et al. (2004, pp. 827–828), a finite verb was deemed to be a mandatory element in a clause, and non-finite verbs were considered to be part of a verb phrase within a clause clustered around a finite verb, although in some studies (e.g., Hakulinen et al., 1996) or descriptions of Finnish grammar (e.g., Karlsson, 2015) also some structures clustered around non-finite verb forms have been considered clauses. As the texts were first split into sentences, and this segmenting was considered reasonably reliable, it was decided to look for clauses within sentences.
However, splitting the data into clauses proved to be problematic. In the first place, not all sentences contained a grammatical clause. In some sentences, especially with the lower proficiency levels, verbs could be completely missing or determining the presence or absence of finite verbs could require interpretation. Some of these verbless sentences were created by punctuation that seemed to split a grammatical clause into two sentences (as in example (2)). Others, especially among the higher proficiency levels, seemed to be stylistically motivated and to intentionally lack a finite verb (see example (6)). With some of these sentences, context was needed in order to choose between several interpretations (as in example (7)), in which the words soitin (musical_instrument.NOM or call.PAST.1SG) and vasta 'just' could have more than one meaning and could be labelled as more than one part of speech: The word vasta could also be a misspelled form of vasta-a (answer.PRS.3SG or answer.INF). Additionally, there were sentences containing only non-finite verb forms, such as infinitives (example (8)), participles, or a negation verb without the main verb.5
(6) Ensimmäinen työpäivä ja hetkessä se onkin ohi. Sitten viikko ja kuukausi.
'First day of work and suddenly it's over already. Then a week and a month.' (F-816, adult C1)
sinä
just/answer
soitin.
musical.instrument/I.called
'Then you answer the ringing phone.' (A possible interpretation) (F-249, adolescent A1)
Kävel-lä
walk-INF
luontossa,
in.nature
katso-a
look-INF
kauniita
paikkoja,
nautti-a
enjoy-INF
meren-
of.sea-
järven
of.lake
vettä.
'To walk in nature, look at beautiful places, enjoy the sea or lake water.' (F-659, adult B1)
It was also problematic because in sentences with more than one finite verb, it was not always clear how many clauses the finite verbs should be divided into. As in example (9), there could be two finite verbs (i.e., ei saa 'may not' and saavat 'may'), but it was not clear if there were two clauses.
get[PRS.3SG]
saa-vat
get-PRS.3PL
ol-la
be-INF
kauan
nettissä
on.the.web
'may not children may be on the internet for a long time.' (F-018, adolescent A1)
Thirdly, coordinators and subordinate conjunctions were sometimes used to connect segments that did not fall within the definition of a clause. As coordinators can be used to connect both clauses and phrases, segments without a finite verb could be interpreted as phrases coordinated with an element in the preceding clause. Another interpretation could be, as in example (10), that there are two coordinated clauses of which the latter is elliptic: The word kielettyä 'forbidden' could be interpreted as an adjective coordinated with sallittua 'allowed' in the preceding clause or as an elliptic clause mutta [että kännykän pitely on] koulussa kielettyä 'but [that holding a mobile is] at school forbidden'.
toivomme
we.hope
että,
kännykän
a.mobile
pitely
sallittua,
mutta
koulussa
at.school
kielettyä.
'we hope that, holding a mobile is allowed, but at school forbidden.' (F-736, adolescent A2)
Regarding the use of subordinate conjunctions, this could create dependent clauses without a grammatical main clause (as in example (11)) or elements beginning with a subordinator but not containing a verb (see example (12)). We will return to this issue when exploring the T-units in the data.
tapahtuu
talvella.
in.winter
'a big problem if it happens in the winter.' (F-657, adult B1)
Alaastella
in.primary.school
mukaan
kouluun,
sellaiset
säännöt.
'In primary school, it is not allowed to bring to school because such rules.' (F-200, adolescent A1)
To evaluate the frequency and significance of these problems, the number of sentences without a finite verb was counted. These sentences were found on all proficiency levels, and also in the L1 texts (Table 4), although they were most common on the lower proficiency levels in the adult learner data. Other sentences considered problematic were counted after coding the T-units into the data.
Number and percentage of sentences containing at least one finite verb, no verb, or at least one non-finite or ambiguous verb form.
Finite verb
No verb
Adult A1 256 80 53 16 13 4 322
Adult A2 300 81 67 18 5 1 372
Adult B1 440 94 26 6 3 1 469
Adult C1 525 95 21 4 4 1 550
Adolescent A1 78 90 4 5 5 6 87
Adolescent A2 136 99 0 0 1 1 137
L1 year 7 264 92 19 7 4 1 287
L1 year 9 105 95 6 5 0 0 11
Four possible solutions to these clause-level annotation problems were considered. The first of these was to include only sentences containing grammatical clauses. While this decision would solve the problems of clause-level coding of sentences with no finite verb, it would not solve the issues related to the number of clauses within those sentences in which there was a finite verb. It would also mean excluding one fifth of the sentences in the adult learners' texts on the two lowest proficiency levels. Secondly, consideration was given to the possibility of counting the number of clauses based on the number of finite verbs present in the texts (e.g., Verspoor et al., 2017). Although this would provide a solution to the problem of counting the number of clauses within the sentences containing at least one finite verb form, it would be affected by sentences not containing any finite verbs. The third possible solution was to introduce a new production unit, similar to the sub-clausal element suggested by Foster et al. (2000) for analysing spoken language. While this solution would address issues related to labelling segments without a finite verb, it would introduce two new issues. On the one hand, it would mean that the exact boundaries of these units would become important if one wanted to measure their length or the clause length in words, because all words in these new units would need to be excluded from the word count of the clauses. On the other hand, it would create a need to introduce new measures in which these new units were included. Otherwise, it could entail excluding these new units and their content from the analysis. The fourth solution to the clause-level annotation problems was to also consider segments such as the grammatically incomplete clauses in examples (11) and (12) as attempted clauses and, therefore, to code them as clauses. While this solution would make it possible to include all the data in the analysis with the quantitative measures, it would create segments labelled clauses that do not fall within the original definition, in which a finite verb was required. We will return to this issue in Section 5.
4.4. T-units
A T-unit was defined as a production unit within a sentence consisting of one main clause and all the subordinate clauses connected to it directly or via another subordinate clause. In applying this definition to the data, problems similar to those in segmenting the data into clauses were encountered. First, the use of punctuation created segments in which there seemed to be a sentence boundary within a T-unit, as in example (3). Second, some dependent clauses had a grammatically incomplete clause as their main clause, as in example (11), and some segments beginning with a subordinator were not complete clauses, as in example (12).
Another type of sentence without an apparent main clause was also encountered. In the data, there were sentences that consisted of two clauses, one starting with a subordinator (e.g., koska 'because') and the other with a coordinating conjunction (e.g., tai 'or'), as in example (13). There were also sentences in which a clause starting with a subordinator seemed to be the main clause of the other clause or clauses in the sentences, as in example (14), in which the clause Jos ajattelen 'If I think' seems to be the main clause of two indirect questions rather than a subordinate clause of either of them. With this kind of sentence, analysis of the context is needed to determine the relationship between the clauses.
saisi
kauan,
for.long
surffata
nettissä.
in.net
'Because they should not be for long, or they should not surf the web.' (F-062, adolescent A2)
ajattelen
I_think
missä
geograafisesti
sijaitsee
is.located
mitä
luonnolla
meille
tarjottavana?
to.offer
'If I think where Finland is geographically located and what nature has to offer us?' (F-420, adult B2)
Sentences containing problems with either the number of clauses or their status as an independent or dependent clause were counted. These sentences were encountered throughout the data on all proficiency levels as well as in the L1 texts. Problematic sentences were more frequent in the adolescent learners' texts (between 22% on level A2 and 9% on B1) than in the adult learners' texts (between 13% on level A1 and 5% on C2), and the problems were not limited to the lower proficiency levels or to isolated texts. Rather, examples were spread across the data, and there was at least one problematic sentence in 40% or more of the L2 texts. There were fewer problematic sentences in the L1 data, but at least one such sentence could be found in 32% of the year 8 students' texts.
To resolve these issues, the use of the sentence as a superordinate unit was reconsidered, as some of the problems could have been solved by coding T-units across perceived sentence boundaries. This would, however, have led to treating some punctuation as erroneous, or ignoring it, which would be problematic, given that in writing, the boundaries of production units cannot be indicated by pauses or intonation, as they can in spoken language. Two other issues to be addressed were the coding of grammatically incomplete clauses or sub-clausal units, and their status as independent or dependent. These problems could have been solved by using an alternative production unit instead of the T-unit, namely the AS-unit, introduced by Foster et al. (2000) for analysing spoken language. While this solution would have acknowledged the sub-clausal units and their role in the superordinate units, it would also have disregarded the sentence boundaries the writer had marked with punctuation.
When measuring learner language complexity with quantitative measures based on production units such as words, clauses, sentences, and T-units, it is important to split the data into these units reliably and consistently (e.g., Ellis & Barkhuizen, 2005; Pallotti, 2015). Nevertheless, as the results of this study show, learner language texts cannot always be divided into the aforementioned production units without making exceptions or leaving loose ends. In other words, as Rimmer (2006, p. 508) points out, authentic language does not always fit "into neat pigeon holes". It is therefore important to explicitly define the production units used and to make visible the exceptions allowed or the amount of data omitted. This information should always be included when reporting research findings.
In the present study, a sentence was defined as a segment indicated by the writer with punctuation or other orthographic means. As it was marked by the writer, a sentence was considered relevant also to the writer (cf. Peters, 1983). Therefore, it was selected as the superordinate unit (cf. Bardovi-Harlig, 1992; Ellis & Barkhuizen, 2005), and all the texts were first segmented into sentences, which were then split into clauses. In the clause-level annotation, clause boundaries and information on coordination and subordination, including information about the main clause of each dependent clause, were annotated where possible. Unclear cases were analysed and the number of sentences in which they occurred was counted. All of the words were annotated as belonging to a sentence and all sentences were annotated to contain a minimum of one independent clause (and thus also at least one T-unit), even when the sentence did not contain a finite verb or when it began with a subordinator. While these decisions led to segments not falling within the definition of the intended production units, they ensured that all the data were included in every annotation level and that they would be included in quantitative measures of syntactic complexity in future studies using this corpus.
These solutions leave room for criticism. They do, however, resonate with earlier findings of the difficulty of fitting learner language into these production unit categories (e.g., Foster et al., 2000; Rimmer, 2006), and they seem to suggest that reliance on production units that are not necessarily found in learner language could be one of the reasons behind inconsistencies in the results that have been obtained using these measures (e.g., Housen et al., 2019; Ortega, 2003; Wolfe-Quintero et al., 1998). In light of the results and the findings of other studies, three different solutions could be considered. One is forcing learner language into the categories used in quantitative measures, as was done in this study. Another is introducing new units of measure for quantitative research, as, for example, Foster et al. (2000) have done. A third solution is to analyse learner language from a more qualitative perspective and, for example, look for qualitative changes and development in selected linguistic features, as has been done by Reiman (2011) in a study on the development of transitive constructions in written learner Finnish.
There are a number of limitations to this study. The data were split into the production units by one person only. It was therefore impossible to negotiate problematic segments and calculate inter-coder agreement. Comparing the sentence-level results with two other segmentations revealed, however, only minor differences between segmentations in identifying words and sentences, which suggests that the sentence-level coding could be considered reliable enough. On the clause level, the problematic segments and their frequency of occurrence were based on the interpretations of one annotator; another annotator could have made different decisions and arrived at different results. While high inter-annotator agreement enhances the reliability of coding, having more annotators would not have eliminated the need to interpret parts of learner language, to adjust the definitions of production units used, or both.
The target language in this study was Finnish, a morphologically rich language, and it is possible that some of the ambiguities are language-specific. The data used in this study come from a heterogeneous group of learners with different proficiency levels. Some of the segmenting difficulties, such as those related to unsystematic use of punctuation, may also be related to the nature of the data. These issues, nonetheless, should be taken into account when making comparisons between studies within one language or studies on different target languages.
The level of detail in learner language coding and in reporting the process naturally depends on the aims and the research questions of each individual study. Nevertheless, segments that are problematic for coding in the data and their potential effect on result, should always be acknowledged. This is essential for accumulating evidence on the development of complexity and for comparability across studies.
Segments which are problematic for coding could also be seen as potential sources of new information, and they could prove to be worth studying in more detail if a more qualitative approach to investigating complexity was adopted. Analysing the actual structures used by learners instead of forcing all learner language into predefined production unit categories could give new insights into the development of learner language and its complexity.
1The standard Finnish spelling is to separate the number and the unit.
2CEFLING = Linguistic basis of the Common European framework for L2 English and L2 Finnish (http://www.jyu.fi/hytk/fi/laitokset/kivi/tutkimus/hankkeet/paattyneet-tutkimushankkeet/cefling).
3For challenges in using the same rating scales for L1 and L2 texts, see, for example, Toropainen et al. (2012).
4It is available under an open licence at http://turkunlp.github.io/Finnish-dep-parser/. For this study, the branch 'master' updated May 9, 2016 was used.
5In Finnish, negation is expressed not with an invariable negation word but with a negation verb (e.g., Karlsson 2015: 82) that agrees with the subject in person and is followed by the main verb (e.g., Lue-n. read-PRS-1SG 'I am reading.', E-n lue. NEG-1SG read 'I am not reading.').
I would like to thank Maisa Martin, Jarmo Jantunen, the two anonymous reviewers and the editorial team for their valuable comments.
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Mylläri T. Words, clauses, sentences, and T-units in learner language: Precise and objective units of measure?. Journal of the European Second Language Association. 2020;4(1):13–23. DOI: http://doi.org/10.22599/jesla.63
Mylläri, T. (2020). Words, clauses, sentences, and T-units in learner language: Precise and objective units of measure?. Journal of the European Second Language Association, 4(1), 13–23. DOI: http://doi.org/10.22599/jesla.63
Mylläri T, 'Words, Clauses, Sentences, and T-units in Learner Language: Precise and Objective Units of Measure?' (2020) 4 Journal of the European Second Language Association 13 DOI: http://doi.org/10.22599/jesla.63
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Mylläri, Taina. "Words, Clauses, Sentences, and T-units in Learner Language: Precise and Objective Units of Measure?". Journal of the European Second Language Association 4, no. 1 (2020): 13–23. DOI: http://doi.org/10.22599/jesla.63
Mylläri, T.. "Words, Clauses, Sentences, and T-units in Learner Language: Precise and Objective Units of Measure?". Journal of the European Second Language Association, vol. 4, no. 1, 2020, pp. 13–23. DOI: http://doi.org/10.22599/jesla.63
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text:
Should every candidate who's not in the top 3 or 4 after Iowa votes drop out?
Thread starter Digital Gex
Start date Sunday at 3:16 PM
Digital Gex
The Iowa primary is less than 30 days away and we still have 400 candidates who have had a year to make their case and still haven't moved the needle. The only hope for most candidates, and Warren, is if they gain momentum after having a good showing in Iowa.
So I think it's a fair question to ask at this point, that when the results for Iowa release and the top 4 winners are revealed, would it not be fair to request all the other candidates drop out? At that point, any chances of catching up are evaporated like steamed milk, so I don't think that's an unreasonable view.
If there's a large gap between the top 3 and number 4 I would say only keep the top 3.
I posted this on the other site, the one with the cesspool, and was promptly banned immediately because something something diversity something something racist how could I possibly make such an unreasonable suggestion something something.
I think this is a logical and rational point of view, why waste time with nobodies when we know Iowa is the end of the road? I don't think that's a racist statement nor do I believe I'm being exclusionary, seems like common sense to me.
I'd also say Buttigieg should drop out if he does not place first or second. Klobuchar should drop out too.
HeresJohnny
Every candidate they're running has said and done some shit that is going to make it very hard for them to win in the General. May as well whittle the field down and start trying to put lipstick on a few pigs.
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No because Iowa, New Hampshire, and Nevada are all VERY different demographics and may have wildly different results.
They should at least wait until after Nevada if they have the campaign resources.
Reactions: Joe T.
Digital Gex said:
I don't think that's a racist statement nor do I believe I'm being exclusionary, seems like common sense to me.
Why is this sentence even in this post?
cryptoadam
... and he cannot lie
Only white candidates should drop out. Diversity fuck yeah.
Reactions: brap and HeresJohnny
cryptoadam said:
Ironic that they call their own base racist, no republicans are voting for these candidates.
Reactions: cryptoadam
Nah, that's way too soon. I can see why certain oversensitive people might view that as racist - they view almost everything as racist - but it's a laughable accusation on its surface. Letting Iowa singlehandedly choose the country's potential nominee would only make sense if every state in the country had similar demographics and interests and that's obviously not the case.
If someone wants to hang around after it's been made clear they have very little support by the public then let them embarrass themselves. If they hang around too long the media will probably turn on them the same way it turned on Sanders in 2016 for not dropping out sooner. They won't be doing themselves any favors.
Warren's the biggest spoiler in the race as I see it. Her supporters largely fall in line with the progressives supporting Sanders insofar as they aren't big fans of incremental gains that come from moderates, but she's got the "It's Her Turn" crowd on her side and they don't care about policy or authenticity so much as putting the first woman in the White House - they won't flock to him if/when Warren bows out.
Pete's looked strong in Iowa up until now, but he seems to have dropped significantly in what's considered "the gold standard" of polls there:
Keep running, wasting all that DNC warchest $$$ that could be used in the general.
If you skim the OP without reading the whole thing you may get confused.,
Joe T. said:
I don't think this post makes much sense.
All the front runners for all current major primary states are all the same people, and almost every other candidate has abysmal poll numbers. New Hampshire and SC aren't going to suddenly bring up Mike Bennett for example, he's done, and so is everyone else outside the top 4.
Your post only makes sense if this primary was more competitive and people outside the top 4 had enough support to siphon votes they don't. Almost all the major early primary states have the same 3 to 4 candidates fighting for first place and are placed way ahead of everyone else. So me saying they should drop out isn't making one state decide the nomination, especially since I said top 4 candidates not just one, but because it is actually the only way for any of them to get the momentum needed to climb from the bottom, so if they do poorly what other state is going to help them?
pennythots
warren and sanders need to go ahead and do a cage match so we can reevaluate where either of them are in comparison to biden
Reactions: EverythingsOverrated
autoduelist
The media and DNC effectively will determine the candidate. I don't see how it matters who drops out, given nobody can name them all anyway.
Rentahamster
Rodent Whores
No, let them do what they want and trust the voters to sort it out.
I read it a few times and still don't understand what race or racism has to do with it.
EverythingsOverrated
pennythots said:
I'm hoping for a freestyle rap battle instead
The kid glove approach is definitely a factor that's hurting the Dems, but they made that decision and now they'll have to deal with it.
Democrats have a history of selecting relative unknowns/longshots as their nominees, much more so than Republicans anyway. Bill Clinton got absolutely crushed in Iowa in '92 with less than 3% of the vote, lost the next three states and only won in one of the seven Super Tuesday states. He went on to become president. A lot has changed in those 28 years, but if a candidate has the will to carry on then they should be given the opportunity to continue making their case.
Who knows, maybe Klobuchar could become this year's Bill Clinton. Bernie took the gloves off against both Biden and Warren, hopefully there'll be more of that now that Iowa's around the corner and not whining about how he's hurting the party (seen MSNBC make that case today).
StreetsofBeige
Got to give credit to Butt. At the beginning of the primaries in the summer, he was a nobody and like most candidates had maybe a couple %. Since then he's been slowly building and has made it to every debate.
I posted this on the other site, the one with the cesspool, and was promptly banned immediately because something something diversity something something racist
I was referring to the other place. They think me wanting all candidates to drop out outside of the top 4 is discriminatory.
But Bill Clinton was competitive. This field has candidates who haven't moved the needle for nearly a year, and in some cases are declining. The top 4 have been in their same positions for just as long, only change is them switching between each other.
Well, Bill jumped in late, but I agree, if they're going to continue playing nice there's little point in the less popular candidates remaining in the race. They have to start taking more shots at each other or come up with far more compelling policy ideas/messages to distinguish themselves from the crowd. Trump did it early on and he built on that. In the Democratic field those that took Trump's approach got pushed aside, like Gabbard and Yang.
jason10mm
Biden, Warren, and Sanders have no choice, this is likely the last primary they will be alive to see. The younger crowd are just jockeying for 2024 and 2030.
I kinda want Warren or sanders to take the primary just to be crushed in the general so we can put this whole socialist agenda to bed. A moderate Democrat is so clearly the obvious choice to heat trump to me, it is stunning that only biden (maybe amy klo....whatever) is making that play. Oh well, come 2024 we'll see. If the GOP can shake the religious overtones they should have no trouble locking it in for another 8 years.
Afro Republican
GAF>INTERNET>GAF, BITCHES
Probably so.
This last debate was supposed to be the big one and it was even hyped as much but only 7.8 million or so watched it. People were expecting everyone to cut each others throats and show they were the reason to vote for them. People also expected everyone to gut Biden at the start and then attack each other for the rest of the debate.
Instead everyone seemed like they had overdosed on medication, Biden wasn't the only one stumbling his words, and we had about 3 awkward moments with Warren, Bernie, and Steyer that had the crowd left in silence trying to comprehend what just happened.
Last debate Yang was the only super low polling candidate one and he did nothing but stay safe and push the same policies he had before not rebuking anyone elses ideas, not challenging others policies, not explaining how his policies would work. He may as well have not attended.
Mario Mario
Every Democrat should just drop out


text:
Acmaeoderella rufomarginata é uma espécie de insetos coleópteros polífagos pertencente à família Buprestidae.

A autoridade científica da espécie é Lucas, tendo sido descrita no ano de 1846.

Trata-se de uma espécie presente no território português.

Referências

Ligações externas
 Acmaeoderella rufomarginata - Biodiversity Heritage Library - Bibliografia
 Acmaeoderella rufomarginata - NCBI Taxonomy Database
 Acmaeoderella rufomarginata - Global Biodiversity Information Facility
 Acmaeoderella rufomarginata - Encyclopedia of Life

Coleópteros polífagos de Portugal
rufomarginata
Coleópteros descritos em 1846

redpajama_set_name: RedPajamaWikipedia


text:
Features Biofuel Biofuels
Prince George expands DES
June 16, 2016 - Prince George's district energy system (DES) fuelling many of its downtown buildings has expanded its services to include the heating of two more provincial buildings - the province's courthouse and the Plaza 400 building. The announcement was made by Sinclar Forest Products' Greg Stewart, Prince George Mayor Lyn Hall and John Martin, Parliamentary Secretary to the Minister of Forests, Lands and Natural Resource Operations for the province of British Columbia, during the International Bioenergy Conference and Exhibition in Prince George.
June 16, 2016 By Andrew Snook
Prince George's DES fuelling many of its downtown buildings has expanded its services to include the heating of two more provincial buildings. The announcement was made by Sinclar Forest Products' Greg Stewart
The DES, which was fully commissioned in 2012, currently reduces annual CO2 emissions by 1,820 tons a year. Fuelled mainly by sawmill residuals from the Lakeland Mills sawmill, the DES already generates hot water used for domestic hot water and space heating in the Prince George Civic Centre, City Hall, Prince George Coliseum community arena, Four Seasons Leisure Pool, Prince George Public Library, Two Rivers Art Gallery, RCMP building and the Wood Innovation and Design Centre.
Ninety-six per cent of the energy used in the DES system is supplied by waste heat generated by burning hog fuel from Lakeland Mills, with the remaining power coming from two 2.5MW natural gas back-up boilers that are used when the mill shuts down for maintenance, or if the system requires additional heat during harsh winter months.
More coverage of the 2016 International Bioenergy Conference and Exhibition:
IBCE 2016 comes to Prince George
Bioenergy sector's opportunities and challenges
Bioenergy, an industry in transition
WPAC promotes Canadian wood pellets in Japan
Government invests in biofuel research
Building bioenergy in B.C.
Turning trash into treasure: The story behind Enerkem's success
prince goerge
Fibre availability and access: challenges and opportunities


Beautifully proportioned and very comfortable - not always guaranteed with renovated antique furniture ! From one of our favoured periods of English chair manufacturing - the 1920s/30s. Really good, solid frames that our leather is well suited to, with the original bun feet prominent, and a classic tan leather to show off the shape and character.


The official trailer of war biopic based on the life and legends of Param Vir Chakra award recipient Subedar Joginder Singh has been released.
Versatile Punjabi singer and actor Gippy Grewal took to Twitter to unveil the official trailer of the film.
'Subedar Joginder Singh' is India's first biopic on a Param Vir Chakra Awardee. Subedar Joginder Singh served the British Indian Army pre-independence and later in the Indian Army. He fought for the independence of the nation and then fought to keep that independence intact and before achieving martyrdom he fought for the honour of India, in 1962 when China infiltrated in the country with evil intentions.
It was due to the sheer mental strength that despite the exhausting ammunition and being hit by a bullet on the thigh, Subedar Joginder Singh kept motivating his soldiers against all odds, along with that he single-handedly killed dozens of Chinese soldiers with Bayonets. The Government of India honoured him with the highest wartime gallantry award, Param Vir Chakra posthumously.
Gippy Grewal is playing the role of the martyr. To essay, the character with perfection Gippy underwent a rigorous training and gained the look of the martyr.
'Subedar Joginder Singh' is produced by Seven Colors Motion Pictures in association with Saga Music and Unisys Infosolutions.
It is directed by Simerjit Singh and it is slated to release on 6th April 2018.
The official poster of Gippy Grewal as 'Subedar Joginder Singh' is here! The teaser of Gippy Grewal's 'Subedar Joginder Singh' is out and we are awestruck! Gippy Grewal's first look as 'Subedar Joginder Singh' is astounding!


text:
package com.spike.giantdataanalysis.model.logic.relational.expression.raw;

import java.util.List;

import org.apache.commons.collections4.CollectionUtils;

import com.google.common.base.Joiner;
import com.google.common.base.Preconditions;
import com.google.common.collect.Lists;
import com.spike.giantdataanalysis.model.logic.relational.core.RelationalAlgebraEnum;
import com.spike.giantdataanalysis.model.logic.relational.expression.raw.CommonLists.UidList;
import com.spike.giantdataanalysis.model.logic.relational.expression.raw.DBObjects.TableName;

/**
 * <pre>
insertStatement
  : INSERT
    priority=(LOW_PRIORITY | DELAYED | HIGH_PRIORITY)?
    IGNORE? INTO? tableName
    (PARTITION '(' partitions=uidList ')' )?
    (
      ('(' columns=uidList ')')? insertStatementValue
      | SET
          setFirst=updatedElement
          (',' setElements+=updatedElement)*
    )
    (
      ON DUPLICATE KEY UPDATE
      duplicatedFirst=updatedElement
      (',' duplicatedElements+=updatedElement)*
    )?
  ;
 * </pre>
 */
public class InsertStatement implements DmlStatement {
  public static enum PriorityType implements RelationalAlgebraEnum {
    LOW_PRIORITY, DELAYED, HIGH_PRIORITY;

    @Override
    public String literal() {
      return name();
    }
  }

  public final PriorityType priority;
  public final Boolean ignore;
  public final Boolean into;
  public final TableName tableName;
  public final UidList partitions;

  public final UidList columns;
  public final InsertStatementValue insertStatementValue;
  public final List<UpdatedElement> setList;

  public final List<UpdatedElement> duplicatedList;

  InsertStatement(PriorityType priority, Boolean ignore, Boolean into, TableName tableName,
      UidList partitions, UidList columns, InsertStatementValue insertStatementValue,
      List<UpdatedElement> setList, List<UpdatedElement> duplicatedList) {
    Preconditions.checkArgument(tableName != null);
    Preconditions
        .checkArgument(!(insertStatementValue == null && (setList == null || setList.isEmpty())));

    this.priority = priority;
    this.ignore = ignore;
    this.into = into;
    this.tableName = tableName;
    this.partitions = partitions;
    this.columns = columns;
    this.insertStatementValue = insertStatementValue;
    this.setList = setList;
    this.duplicatedList = duplicatedList;
  }

  @Override
  public String literal() {
    StringBuilder sb = new StringBuilder();
    sb.append("INSERT").append(" ");
    if (priority != null) {
      sb.append(priority.literal()).append(" ");
    }
    if (Boolean.TRUE.equals(ignore)) {
      sb.append("IGNORE ");
    }
    if (Boolean.TRUE.equals(into)) {
      sb.append("INTO ");
    }
    sb.append(tableName.literal()).append(" ");
    if (partitions != null) {
      sb.append("PARTITION (").append(partitions.literal()).append(") ");
    }

    if (insertStatementValue != null) {
      if (columns != null) {
        sb.append("(").append(columns.literal()).append(") ");
      }
      sb.append(insertStatementValue.literal()).append(" ");
    } else {
      sb.append("SET ");
      List<String> literals = Lists.newArrayList();
      for (UpdatedElement updatedElement : setList) {
        literals.add(updatedElement.literal());
      }
      sb.append(Joiner.on(", ").join(literals));
    }

    if (CollectionUtils.isNotEmpty(duplicatedList)) {
      sb.append("ON DUPLICATE KEY UPDATE ");
      List<String> literals = Lists.newArrayList();
      for (UpdatedElement updatedElement : duplicatedList) {
        literals.add(updatedElement.literal());
      }
      sb.append(Joiner.on(", ").join(literals));
    }
    return sb.toString();
  }

}

redpajama_set_name: RedPajamaGithub


text:
William Swinfen Cottrell1
M, #7041
William Swinfen Cottrell married Harriette Annie Evans in 1877 at Chepstow, Monmouthshire, Wales.1
Child of William Swinfen Cottrell and Harriette Annie Evans
George Swinfen Cottrell+1 b. 1886, d. 1957
Margaret Poyntz1
F, #7042
Margaret Poyntz was the daughter of Sir Anthony Poyntz and Elizabeth Huddersfield.1,2 She married Sir John Newton, son of John Newton.2
Her married name became Newton.3
Children of Margaret Poyntz and Sir John Newton
Frances Newton+4 d. 17 Oct 1592
Nazareth Newton+1 d. 16 Apr 1583
Theophilia Newton5
John Newton+3
[S6] G.E. Cokayne; with Vicary Gibbs, H.A. Doubleday, Geoffrey H. White, Duncan Warrand and Lord Howard de Walden, editors, The Complete Peerage of England, Scotland, Ireland, Great Britain and the United Kingdom, Extant, Extinct or Dormant, new ed., 13 volumes in 14 (1910-1959; reprint in 6 volumes, Gloucester, U.K.: Alan Sutton Publishing, 2000), volume X, page 283. Hereinafter cited as The Complete Peerage.
[S35] BLG1965. [S35]
[S6] Cokayne, and others, The Complete Peerage, volume III, page 348.
[S6] Cokayne, and others, The Complete Peerage, volume XII/2, page 854.
Sir Anthony Poyntz1
M, #7043, b. 1480, d. 1535
Sir Anthony Poyntz was born in 1480.2 He was the son of Sir Robert Poyntz and Margaret Woodville.3 He married, firstly, Elizabeth Huddersfield, daughter of Sir William Huddersfield and Katherine Courtenay.2 He married Joan (?)3 He died in 1535.3
He held the office of Sheriff of Gloucestershire in 1507.2 He held the office of Sheriff of Gloucestershire in 1522.2 He lived at Iron Acton, Gloucestershire, EnglandG.2 He held the office of Sheriff of Gloucestershire in 1527.3
Children of Sir Anthony Poyntz and Elizabeth Huddersfield
Margaret Poyntz+1
Sir Nicholas Poyntz+3 d. b Jul 1557
Giles Poyntz3
Ferdinando Poyntz3
Robert Poyntz3 d. b 1520
Thomas Poyntz3 d. b 1520
Mary Poyntz3
Sir Thomas Southwell1
M, #7044, b. circa 1542, d. 1568
Sir Thomas Southwell was born circa 1542 at Meredith, Kent, England.2 He was the son of Sir Robert Southwell and Margaret Neville.2 He married, firstly, Mary Jerningham, daughter of Sir Henry Jerningham and Frances Baynham, before 1561.2 He married, thirdly, Nazareth Newton, daughter of Sir John Newton and Margaret Poyntz, before 1568.1 He married Mary Maunsell, daughter of Sir Rice Mansel and Cecily Danbridgecourt, in 1558.3 He died in 1568.1 His will was proven (by probate) on 30 June 1568.1
He lived at Wood Rising, Norfolk, EnglandG.1 His last will was dated 13 March 1567/68.
Child of Sir Thomas Southwell and Mary Maunsell
Sir Robert Southwell+2 d. 12 Oct 1599
[S3268] Hans Harmsen, "re: Chester Family," e-mail message to Darryl Roger Lundy, 21 August 2008. Hereinafter cited as "re: Chester Family."
Sir Francis Knollys1
M, #7045, b. 1511, d. 19 July 1596
Sir Francis Knollys 2
Sir Francis Knollys was born in 1511. He was the son of Sir Robert Knollys and Lettice Peniston.3 He married Katherine Carey, daughter of William Cary and Lady Mary Boleyn, in 1540.4 He died on 19 July 1596.5
He was appointed Knight, Order of the Garter (K.G.)5
Children of Sir Francis Knollys and Katherine Carey
Lettice Knollys+6 b. 1540/41, d. 25 Dec 1634
Henry Knollys+1 b. c 1542, d. 1582
Mary Knollys6 b. 1542
Edward Knollys6 b. 1546, d. 1580
William Knollys, 1st Earl of Banbury+5 b. c 1547, d. 25 May 1632
Sir Robert Knollys6 b. c 1547, d. 1619
Maud Knollys6 b. 1548
Elizabeth Knollys+4 b. 1549
Sir Francis Knollys6 b. c 1550, d. 1648
Richard Knollys6 b. 1552, d. 1596
Anne Knollys+7 b. c 1553
Sir Thomas Knollys6 b. 1558, d. a Aug 1596
Catherine Knollys+4 b. 1559
Dudley Knollys6 b. 1562
[S3409] Caroline Maubois, "re: Penancoet Family," e-mail message to Darryl Roger Lundy, 2 December 2008. Hereinafter cited as "re: Penancoet Family."
[S6] Cokayne, and others, The Complete Peerage, volume I, page 400.
[S6] Cokayne, and others, The Complete Peerage, volume IV, page 160.
Isabella Irby1
F, #7046, b. before 1669, d. circa December 1685
Isabella Irby was born before 1669. She was the daughter of Sir Anthony Irby and Hon. Katherine Paget.1 She married William Paget, 6th Lord Paget (of Beaudesert), son of Sir William Paget, 5th Lord Paget (of Beaudesert) and Lady Frances Rich.1 She died circa December 1685.1 She was buried on 18 December 1685 at West Drayton, London, EnglandG.1
Her married name became Paget.1 After her marriage, Isabella Irby was styled as Baroness Paget of Beaudesert. She was also known as Isabel Irby.
Child of Isabella Irby and William Paget, 6th Lord Paget (of Beaudesert)
Hon. William Paget2 b. b 1685
[S8] BP1999 volume 1, page 72. See link for full details for this source. Hereinafter cited as. [S8]
Helen Tolmie Dick Bayly1
F, #7047, d. 8 October 1935
Helen Tolmie Dick Bayly was the daughter of General John Bayly and Jane Crum-Ewing.1 She married Charles John Cathcart Douglas, son of General Sir John Douglas of Glenfinart and Lady Elizabeth Cathcart, on 17 November 1880.1 She died on 8 October 1935.1
From 17 November 1880, her married name became Douglas.1
Hon. Katherine Paget1
Hon. Katherine Paget is the daughter of William Paget, 4th Lord Paget (of Beaudesert) and Lettice Knollys.1 She married Sir Anthony Irby, son of Sir Anthony Irby and Elizabeth Peyton, on 19 August 1641.2
From 19 August 1641, her married name became Irby.1
Children of Hon. Katherine Paget and Sir Anthony Irby
Anthony Irby+2 d. 1684
Isabella Irby+1 b. b 1669, d. c Dec 1685
Margaret Samwell1
Margaret Samwell is the daughter of Richard Samwell.1
Child of Margaret Samwell and Thomas Catesby
Mary Catesby+1 d. 3 Nov 1734
Richard Samwell1
Richard Samwell is the son of Sir Richard Samwell. He married Frances Wenman, daughter of Thomas Wenman, 2nd Viscount Wenman.
He lived at Upton, Northamptonshire, EnglandG.1
Children of Richard Samwell and Frances Wenman
Sir Thomas Samwell, 1st Bt.+
Frances Samwell+
Mary Samwell+
Child of Richard Samwell
Margaret Samwell+1


Lead Application Developer @ VML, specializing in Web Application Development, Mobile Development, and Game Development.
Co-Founder of 4 enterprise software startups. x-SAP and NTT innovation and sales executive. Advisor to large enterprises on incubator strategy and startup engagement, focused on Robotics, AI, Digital Health, Dental, Blockchain.
Having helped Fortune 500 companies to scale rapidly in different parts of the world, I am currently helping leaders to maximize their business potential by leveraging best of digital solutions and Fortune 500 best practices. Reach out to me on topics such a building a culture of innovation, robotics in business processes, cognitive analytics, process mining, applying machine learning, artificial intelligence and other digital solutions. Having led operations through 5 US$ 10+ billion mergers, acquisitions, numerous outsourcing contracts and Hewlett Packard split into two Fortune 500 companies, I am also an expert in MADO and large scale transformations. Focused on enabling entrepreneurs and startups to take a Quantum Leap by leveraging general management best practices from Fortune 500.
Techie by brains, Traveller, photographer by passion 15+ years worked with startup and SAP, co-founded photographic society of Bangalore, in India.
Technology start up company. small business owner, I run and manage a small business IT consulting firm here in New York metropolitan area. I've been involved in many different types of projects for small business to large corporations everything from Weight Watchers TJ Max, to United nations . I've consulted and done work for these companies doing IT services. I provide a quality service.


H M W S INC. (Harness).
THAITIAN CIGARETTE AND TOBACCO CORPORATION.
KUEHNE + NAGEL LOGISTICS SOLUTIONS, INC.
LAKSHMI (BEMT) / ANA PUMA.
CLARK AIRPORT SUPPORT SERVICES CORP. (CASSC).
AVANTI GARAGE DOOR OPENERS INC.
ACCESS POINT MEDICAL PLAZA (APMAC).
DRAGON 24 DESIGN & PRINTING CORP.
INTERTECH MARKETING, SALES AND SERVICES INC.
ABB BRETTO'S FOODS INT'L. INC.
UNION BANK OF THE PHILS.
M.A.G. TRAVEL & TOURS INC.
D T INTERACTS PHILS., INC.
CSMFG INC./ AL Gustava Gerhart.
Customs Sports & Leisure (CSL).
WELL MADE SINK AND FURNITURE INC.
SOLID STATE LED LIGHTING, INC.
PHOTO FUSION DIGITAL PRINTING INC.
M.I.S. INT'L ASIA TECH. INC.


text:
Matt Zarb-Cousin: Gambling controls have worked. Will the Government take them further?
By Matt Zarb-Cousin
The Tories have an historic opportunity to turn Britain into a world leader in gambling harm prevention with a new Gambling Act
Philip Smith: As the Government's gambling review gets under way, here are some principles for action
Scott Benton: We are right to reform the UK's gambling laws – but we must not drive punters into the arms of the black market
Michael Dugher: Ministers deserve credit for their evidence-led review of betting and championship of safer gambling
Richard Holden: The age at which National Lottery games can be played should be raised to 18
Matt Zarb-Cousin is the Director of Clean Up Gambling. This is a sponsored post by Clean Up Gambling.
"Stop the FOBTs" (fixed odds betting terminals) was an initiative launched in early 2013 by the Campaign for Fairer Gambling, founded and funded by gambling expert Derek Webb, who brought me in as a campaign consultant and spokesperson.
Its objective, an FOBT stake reduction from £100 to £2 per spin, was finally introduced in April 2019. Recently released Gambling Commission statistics show a full year of data to March 2020, with the impact of the Covid-19 lockdown spanning just 11 days of this timeframe.
In line with the trend, the number of betting shops declined six per cent from 8,320 to 7,681 due to bookmakers converting shop gamblers to more profitable remote gambling with a lower overall tax burden and lower employment costs.
This contrasts sharply with the Association of British Bookmakers' (ABB) KPMG report, which Philip Hammond referred to in his time as chancellor. It forecast a decline to as few as 4,500 shops.
As reported in The Guardian, betting shop machine revenue declined 41 per cent to £1,075 million – down £751 million from £1,826 million. Over-the-counter betting on races and sports also decreased.
There was a decline in revenues on games at stakes in excess of £2 by £1,142 million, but an increase in revenue at stakes of £2 and below by £390 million. By far the greatest crossover was players trading down stakes, but staying on betting shop machines.
This confirms the notion that placing a restriction on a specific gambling product can have the desired impact of reducing losses at that activity and reducing losses in total.
All the evidence indicates that there was no consequential crossover from the £751 million decline in betting shop machine revenue to any other land-based or remote sector.
Gaming machine revenues in casino, bingo and family entertainment centres remained constant, while an increase in adult gaming centre revenues continued the trend of the past several years. All casino table game revenues declined, with a minor increase in electronic gaming failing to offset the resulting overall decline in casino revenues from £840 million to £801 million.
Remote gambling revenues for bingo and casino increased in line with previous years, and while betting revenues at first sight look like a large increase, this was only due to betting result in the year prior being less helpful to the bookies than usual. It is therefore consistent with the long-term trend of increased remote betting revenues.
National Gambling Treatment Service statistics show the activities that disordered gamblers refer to when presenting for treatment. The '"betting shop machine" references decreased from 26.4 per cent in 2018-2019 to 20.3 per cent in 2019-2020, a relative decrease of 23 per cent — the most significant change in any activity. This data clearly confirms the logic that loss reduction results in harm reduction.
Webb continues to support the gambling reform agenda, and funds Clean Up Gambling. Decreasing FOBT stakes was a sensible change to outdated legislation, and has had a hugely positive impact. We look forward to the Government's comprehensive review of our outdated gambling laws, and its thorough consideration of the evidence.
gambling KPMG online gambling
Thames Freeport: River regeneration – an economic opportunity not to be missed
Frank Zilberkweit: Restricting personal choice by banning fur would not be a Brexit bonus
Laurence Robertson: The gambling review is essential. But MPs must be wary of the law of unintended consequences.
Tim Pilcher: How simple connectivity vouchers can empower Britain's businesses right now
The Health Foundation: What should the Government do to tackle obesity? Find out in our new podcast.
Norman McKenzie-Richmond: A pro-business Government must reverse course on tax-free shopping
4 comments for: Matt Zarb-Cousin: Gambling controls have worked. Will the Government take them further?


text:
PST 101 - Introduction to Retail Sales Taxes
PROVINCIAL RETAIL SALES & USE TAXES
Who has Them ?
Prior to the implementation and recent expansion of the Hamonized Sales Tax (as described further below, the "HST"), nine out of the ten Canadian provinces levied provincial sales and use taxes, which are colloquially referred to as either "PST" (for "provincial sales tax") or "RST" (for provincial retail sales tax).
With the expansion of the HST to Prince Edward Island in 2013, only three Canadian provinces now levy a stand-alone PST, these being each of: British Columbia, Saskatchewan, and Manitoba. Quebec now employs the "Quebec Sales Tax" (the "QST"), which is almost fully harmonized with the federal Goods and Services Tax ("GST"), but does contain some important differences.
Ontario, along with the four Atlantic Provinces of New Brunswick, Nova Scotia, Prince Edward Island and Newfoundland & Labrador all employ the HST (at varying rates), which is fully harmonized with the GST.
The last Canadian province, Alberta, does not levy a PST, and neither do Canada's three territories of Yukon, Nunavut or Northwest Territories.
Currently there are no Canadian cities or municipalities imposing sales taxes.
How do Sales Taxes Work ?
Sales taxes (like those employed in the various Canadian provincial PST systems) are generally levied on the sale, or importation into a province, of tangible personal property, and a limited range of services for final consumption or use within a particular province.
Purchasers are generally required to pay PST at the time of purchase, unless the goods are purchased for resale or a specific exemption applies (e.g., basic groceries, books, and production equipment in some provinces).
Although PST is imposed on the purchaser, provincial vendors are generally required to register to collect and remit the PST to the applicable provincial government. Persons bringing taxable goods into a province for their own use are generally required to self-assess and remit the applicable tax directly to the province.
Where the goods are shipped from outside of Canada, clearing Canadian Customs, all PST provinces (except PEI) have entered into collection agreements with the federal government. Under the PST collection agreements the federal government will collect PST on non-commercial taxable importations where the imported goods are also subject to GST. Persons not covered by the PST collection agreements (i.e., most businesses) should ensure that they properly self-assess PST where applicable.
Definitions and Tax Bases
Saskatchewan, Manitoba, and British Columbia each levy PST on tangible personal property (TPP).
While the generally stated tax base in each of the three PST provinces is therefore fairly consistent, the differences lie in the details of the particular systems. Accordingly, one sees that some items will generally do not qualify as tangible personal property (including intangibles such as accounts receivables, goodwill, trademarks, licenses, and shares), with each particular province treating these exceptional situations, in some degree of another, differently.
For example, the manner in which "services" are taxed in these various provincial systems varies. Manitoba limits taxable services to things like telecommunication services, repair and maintenance labour and accommodations, while Saskatchewan levies tax on a much broader range of services.
Computer software and telecommunications are also generally taxed across all jurisdictions, but the rules vary by those jurisdictions.
Related Party Transfers
All provinces that impose PST have special relieving provisions which deal with transfers between related parties and in special circumstances, transfers between unrelated parties. Transfers between unrelated parties, however, are generally only exempt when the purchaser is a corporation and, as consideration for the purchase, it issues shares to the vendor. Generally, the vendor must also hold onto the shares for a minimum of 6 to 8 months.