text:
\section{}\label{}

\section{Introduction}
\label{sec:intro}
Similar to the methodology in~\cite{gezici2022measuring}, in the context of this paper same educational queries are used for the bias analysis and the abbreviation of~\emph{YVRP} is used for the YouTube video result page. The gender of the narrator is similarly explored in this work, and the main aim is to label the videos according to the narrator's~\emph{perceived} gender. Unlike~\cite{gezici2022measuring}, in this paper a given video is not labelled with a single gender label rather, with a probability distribution of male and female labels that could depict a more realistic~\emph{perceived} gender spectrum of a given video. Note that the~\emph{perceived} gender annotation will be automatically determined using the voice information of narrator(s) instead of manual annotation. For the detailed annotation procedure, please refer to our previous work~\cite{gezici2022measuring}.

Gender stereotypes are a common source of bias that emerge when an individual or a group is systematically treated favourably or unfavourably, referring to~\emph{individual} or~\emph{group fairness} respectively and  there is a need to investigate gender representation in educational resources. In fact, the European Institute for Gender Equality states that gender stereotypes still exist in educational materials~\cite{EU}. There are some guidelines on how to evaluate diversity in educational materials, for example  Michigan in the United States issued a report in 2020 as a guidance for the experts in evaluating instructional materials in terms of bias~\cite{Center2020ToolsMaterials}.

YouTube states that they audit their machine learning systems to avoid cases leading to gender discrimination~\cite{YouTube}. However, this does not guarantee that the returned videos are not biased towards a specific gender. In this study, our aim is to  investigate educational videos returned by YouTube in terms of possible gender bias via objective measures. We focus on~\emph{group fairness}  since we investigate if the online  materials are affected by societal stereotypes about gender in the context of education. Moreover in~\emph{group fairness}, we specifically focus on~\emph{statistical parity}, \emph{demographic parity} or more generally known as~\emph{equality of outcome}, i.e. given a population divided into groups, the groups in the output of the system should be equally represented. In the scope of this work,~\emph{equality of outcome} is a more appropriate standard since we require~\emph{equal} gender representations in results.
Our main aim in this study is to detect bias with respect to~\emph{equality of outcome} using the~\emph{perceived} gender of narrators in videos returned by YouTube in response to educational queries comprising of keywords regarding some educational field. For this purpose, we use educational queries that are derived from the course modules
of STEM and NON-STEM fields.

Our contributions can be summarised as follows:

\begin{enumerate}

\item We propose an automated pipeline to annotate narrators' genders in YouTube videos which could be used for large-scale bias analysis.

\item We present~\emph{two fine-grained measures of bias} which are explained in Section~\ref{sec:measures_gender_bias_automatic} in detail that represent the gender spectrum of a given video with a probability distribution which is a more realistic representation, and generate bias values which are symmetric and easy to interpret.

\item We implement our fine-grained bias measures to \emph{investigate possible perceived gender bias} as well as \emph{compare the relative bias} for educational searches in YouTube about different majors from STEM and NON-STEM fields.

\item We further investigate the source of~\emph{perceived} gender bias in YouTube for educational searches owing to the proposed automated pipeline.

\end{enumerate}

\section{Gender Bias Evaluation Methodology}
\label{sec:gender_bias_evaluation_methodology_source}

This section describes the methodology for evaluating~\emph{perceived} gender bias without a binary gender assumption. The bias measures proposed in~\cite{gezici2022measuring} are adapted to using male/female probability values instead of single~\emph{perceived} gender labels. Further, a similar evaluation procedure presented in~\cite{gezici2022measuring} is fulfilled for identifying potential bias as well as tracking the source of bias (if applicable) associated with those adapted measures.

\subsection{Preliminaries and Research Questions}
\label{sec:prelim}

Using an automated approach to obtain the~\emph{perceived} gender probability distributions of videos through voice information, this work mainly aims to following research questions.
The first research question is:

\begin{description}
    \item[RQ1:] On a~\emph{perceived} male-female gender~\textbf{spectrum}, does YouTube return~\emph{biased}~\emph{YVRP}s in response to various educational queries~\textbf{using more fine-grained measures}?
\end{description}

The second research question is:

\begin{description}
 \item[RQ2:] Is there a~\emph{significant} difference in~\emph{perceived} gender bias that is computed in a~\textbf{more fine-grained manner} in~\emph{YVRP}s returned in response to STEM vs. NON-STEM educational queries?
\end{description}

The third research question is:

\begin{description}
    \item[RQ3:] Do different cut-off values affect the existence of~\emph{perceived} bias and magnitude of bias difference by~\textbf{using more fine-grained measures} between STEM and NON-STEM fields?
\end{description}

The fourth research question is:

\begin{description}
    \item[RQ4:] Do different cut-off values affect the magnitude of~\emph{perceived} bias of STEM and NON-STEM fields separately that is measured~\textbf{in a more fine-grained manner}?
\end{description}

The last research question which is totally a new question that could not be answered in our previous work~\cite{gezici2022measuring}. In the context of this paper, the source of bias (if exists) is investigated. If the bias measured in the full~\emph{YVRPs} is consistent with the top video search results, i.e. especially in top-3, top-10 since these search results attract users' attention the most, then it can be inferred that the bias comes from the data itself. If there are some differences between those bias results, then the ranking algorithm could also be blamed. Note that the data and ranking algorithm both could be responsible for the biased~\emph{YVRPs}.

The fifth research question is:

\begin{description}
    \item[RQ5:] What is~\textbf{the source of bias (if exists)}, does it come from the input data, or the ranking algorithm?
\end{description}

\subsection{Measures of Bias}
\label{sec:measures_gender_bias_automatic}

\begin{table*}
\centering
\caption{Symbols, functions, and labels used throughout this paper}
\resizebox{0.8\linewidth}{!}{
\begin{tabular}{ l l }
\hline
\hline
{Symbols}\\
\hline
\hline
$Q$   & set of queries. \\
$q$     & a query $q \in Q$. \\
$r$     & a ranked list of the given \emph{YVRP} (list of retrieved videos). \\%(list of documents).
$r_i$   & the video in $r$ retrieved at rank $i$.\\
$|r|$ & size of $r$ (number of videos in the ranked list).
\\
$n$     & number of videos considered in $r$
(cut-off).\\
\hline
\hline
{Functions}\\
\hline
\hline
$prob_{G_{m}}(r_i)$                & returns the probability value corresponds to male gender for $r_i$.\\
$prob_{G_{f}}(r_i)$                & returns the probability value corresponds to female gender for $r_i$.\\
$f(r)$                  & an evaluation measure for \emph{YVRP}s.\\
\hline
\hline
{Labels}\\
\hline
\hline
$G_m$ & \emph{perceived} male gender. \\
$G_f$ & \emph{perceived} female gender. \\
$G_{not\_rel}$ & not-relevant wrt a query. \\
$G_{N/A}$ & N/A - gender annotation is not applicable. \\
\hline
\hline
\end{tabular}}
\label{table:notation_gender}
\end{table*}

Let $q \in Q$ be the set of educational queries about the majors in STEM and NON-STEM fields.
When a query $q \in Q$ is issued to YouTube, YouTube returns a \emph{YVRP} $r$. The probability value associated with the~\emph{perceived} gender of the $i$-th retrieved video $r_i$ with respect to $q$ is defined as $prob_{G_{m}}(r_i)$ for male and $prob_{G_{f}}(r_i)$ for female. For reference, Table~\ref{table:notation_gender} shows a summary of all the symbols, functions and labels used throughout this paper.

In the scope of this paper, similar to Section~\cite{gezici2022measuring}, the main aim is to satisfy group fairness criteria of~\emph{equality of outcome} where male and female genders should be equally represented in~\emph{YVRPs}. Thus the~\emph{perceived} gender bias is measured as the difference between the representation of male and female genders.

Formally, the~\emph{perceived gender} bias in a~\emph{YVRP} $r$ is measured as follows:

\begin{linenomath*}
\begin{equation}
\label{eq:delta_bias_online}
    \Delta_f(r) = f_{G_{m}}(r) - f_{G_{f}}(r)
\end{equation}
\vspace{0.2em}
\end{linenomath*}

For the function $f(r)$, two bias measures that are proposed in~\cite{gezici2022measuring} are adapted to using probability scores instead of single~\emph{perceived} gender labels in the scope of this paper. Note that the videos annotated with $G_{not\_rel}$ and $G_{N/A}$ are initially discarded before the bias score computations.
The two adapted measures of~\emph{representation} and~\emph{exposure} are denoted by~$\mathcal{R}ep_{prob}@n$ and $\mathcal{E}xp_{prob}@n$ respectively. The first adapted measure of bias,~$\mathcal{R}ep_{prob}@n$ which computes a bias score using probability values associated with the~\emph{perceived} gender label of~\emph{male} as follows:

\begin{linenomath*}
\begin{equation}
\label{eq:rep_prob}
    \mathcal{R}ep_{prob}{G_{m}}@n =
    \frac{1}{prob_{mf}}\sum_{i=1}^n prob_{G_{m}}(r_i)
\end{equation}
\vspace{0.2em}
\end{linenomath*}

Note that~$\mathcal{R}ep_{prob}{G_{f}}@n$ is computed in the same way. The following equation by substituting Eq.~\eqref{eq:rep_prob} in Eq.~\eqref{eq:delta_bias_online}:

\begin{linenomath*}
\begin{equation}\label{eq:bias_rep_prob}
    \Delta_{\mathcal{R}ep_{prob}@n}(r) =
    \frac{1}{prob_{mf}}{\sum_{i=1}^n\left(prob_{G_{m}}(r_i) - prob_{G_{f}}(r_i)\right)}
\end{equation}
\vspace{0.2em}
\end{linenomath*}

Since the first bias measure of~$\mathcal{R}ep_{prob}@n$ has a weak sense of rank information, i.e. all the positions contribute to bias score in an equal manner, the second measure is presented by adapting~$\mathcal{E}xp@n$ that was proposed in~\cite{gezici2022measuring} to using probability scores. The second adapted measure of bias,~$\mathcal{E}xp_{prob}@n$ which computes a bias score in terms of exposure, using probability values associated with the~\emph{perceived} gender label of~\emph{male} as follows:

\begin{linenomath*}
\begin{equation}
\label{eq:dcg_exposure_prob}
    \mathcal{E}xp_{prob}{G_{m}}@n =
    \sum_{i=1}^{n}\frac{1}{\log(i + 1)}(\frac{prob_{G_{m}}(r_i)  }{prob_{G_{m}}(r_i) + prob_{G_{f}}(r_i)})
\end{equation}
\vspace{0.2em}
\end{linenomath*}

Note that $\mathcal{E}xp_{prob}{G_{f}}@n$ is computed in the same way. The following equation is obtained by substituting Eq.~\eqref{eq:dcg_exposure_prob} in Eq.~\eqref{eq:delta_bias_online}:

\begin{linenomath*}
\begin{equation}
\label{eq:bias_exposure_prob}
    \Delta_{\mathcal{E}xp_{prob}@n}(r) =
    \sum_{i=1}^{n}\frac{1}{\log(i + 1)}(\frac{prob_{G_{m}}(r_i) - prob_{G_{f}}(r_i)}{prob_{G_{m}}(r_i) + prob_{G_{f}}(r_i)})
\end{equation}
\vspace{0.2em}
\end{linenomath*}

The scores of the proposed measures are easy to interpret, for a given ranked list, the scores of two gender groups sum up to 1. If the bias scores are interpreted with respect to the~\emph{equal} representation using~$\mathcal{R}ep_{prob}@n$, then it can be inferred which gender group is more/less represented than the desired representation. Same holds true for the exposure measure, $\mathcal{E}xp_{prob}@n$, which determines if a gender group is more or less exposed than the desirable situation of the~\emph{equal} exposure. For interpreting the results, if the value of $0.5$ which is the desired case, is subtracted from the measure scores of male and female gender for a given list, then the remaining bias scores of each gender group are symmetric. Same holds for the exposure measure. Additionally, these adapted bias measures are expected to compute smoother and more realistic bias scores owing to the probability scores of the~\emph{perceived} gender groups instead of single labels which are too deterministic for annotating real datasets.

After the computation of adapted representation and exposure bias scores, the mean bias (MB) and mean absolute bias (MAB) of these measures can be further computed over a set of queries in the dataset to aggregate the bias results. MB score of STEM field computes a mean value over all the STEM queries' scores for the corresponding measure, whereas MAB computes a mean value over all the absolute value of the measure scores for the STEM queries. Note that MB shows towards which~\emph{perceived} gender the results are biased and MAB solves the limitation of MB if different queries have bias contributions with opposite signs and cancel each other out. Thus, MB and MAB measures are complementary for aggregating the results and interpreting those results in a proper way.

Please note that in the context of this paper, the probability scores correspond to each~\emph{perceived} gender label, i.e. $prob_{G_{m}}(r_i)$ and $prob_{G_{f}}(r_i)$ for male and female respectively, of a given video is computed merely based on the narrators'~\emph{perceived} gender by using voice information of the narrators. For the details about the automated annotation procedure, please refer to~\ref{sec:gender_bias_annotation_procedure}. Since the probability scores are leveraged, there is no gender binary assumption throughout this paper.

\subsection{Quantifying Bias}

Using the measures of bias defined in Section~\ref{sec:measures_gender_bias_automatic}, the~\emph{perceived} gender bias of the STEM and NON-STEM fields is first measured in~\emph{YVRP}s returned in response to the educational queries in various majors, and then a comparative evaluation is fulfilled.

\begin{itemize}
\item \textbf{Collecting~\emph{YVRP}s.}
The same educational query list was used to crawl~\emph{YVRP}s, for the complete query list, please refer to~\cite{gezici2022measuring}. For each query, top-200 video results returned by YouTube UK in~\emph{incognito mode} was crawled by using the YouTube Data API v3~\footnote{\url{https://developers.google.com/youtube/v3/docs}}. Note that the data collection process was done in a controlled environment such that the queries were sent to YouTube via the YouTube API by avoiding long time-lags. After the crawling, all the video results related to the majors in both STEM and NON-STEM fields, they were automatically assigned with probability scores of each~\emph{perceived} gender group using the voice information of the narrators.

\item \textbf{Bias Evaluation.}
The bias scores are computed for every~\emph{YVRP} with two adapted bias measures with four different cut-off values: $\mathcal{R}ep@n$ and $\mathcal{E}xp@n$ for $n$ = $3$, $10$, $20$, and $200$, e.g. full list where $|r|$ = 200. Then, the results are aggregated using the MB and MAB.
Additionally, first the existence of bias for each field is examined, then the bias results of STEM and NON-STEM fields with different measures and cut-off values are compared. Subsequently, the impact of different cut-off values is investigated on bias scores of STEM and NON-STEM fields. Finally, the source of bias is investigated by comparing the top video search results with the full list, i.e. top-3, top-10 vs. top-200.

\item \textbf{Statistical Analysis.}
To identify whether the bias measured is not due to noise,
a one-sample t-test is applied. Note that since the sample size is sufficiently large ($> 30$), according to the central limit theorem the sampling distribution is considered normal~\cite{Kwak2017CentralStatistics}.
If this hypothesis is rejected, hence there is a significant difference and it can be claimed that the~\emph{YVRP}s of the evaluated field, STEM or NON-STEM is biased. The difference in bias measured across the two fields is further compared using a two-tailed~\emph{independent} t-test.
In addition to the statistical significance, namely p-values, effect sizes are also reported using Cohen's d. Statistical significance helps to examine whether the findings show systematic bias or they are the result of noise, whereas effect sizes provide information about the magnitude of the differences. Thus, both p-values and effect sizes provide complementary information for the interpretation of the results.

Apart from these, to investigate the effect of different cut-off values on bias results in the same field, STEM or NON-STEM, a two-tailed~\emph{paired} t-test is computed since the same query set is examined for different cut-off values and for two different annotation models. Moreover, Bonferroni correction is further applied~\cite{sedgwick2012multiple} for multiple hypothesis testing since there are 60 hypotheses in total in the context of cut-off value and annotation model analyses. Thus, without the Bonferroni correction, with the significance level, $ \alpha = .05$ and 60 hypotheses, the probability of identifying at least one significant result due to chance is around $0.95$ which means that the results could be misleading. Hence, the Bonferroni correction is also applied for more reliable results. Note that for the significance level where $\alpha = .05$, and with the Bonferroni correction new $\alpha = .0008$. Thus, Bonferroni correction rejects the null hypothesis for each p-value ($p_i$) if $p_i <= .0008$ instead of $.05$. For the significance level where $\alpha = .01$, with the Bonferroni correction new $\alpha = .0002$.

\end{itemize}

\section{Experimental Setup}
In this section, first dataset information, then the annotation procedure, and lastly the~\emph{perceived} gender bias results will be provided based on the proposed method as described in Section~\ref{sec:gender_bias_evaluation_methodology_source}. In addition to the existence of bias, source of~\emph{perceived} gender bias is investigated as well.

\subsection{Dataset}
\label{sec:gender_bias_dataset_automatic}

The same educational query list was used to crawl the dataset.
For the crawling, YouTube Data API v3 was utilised. Note that crawling with a Python implementation using the YouTube Data API v3 was fast enough that it does not create noticeable time lags between queries which could affect the bias analysis. Initially, several API keys were created for free. Then, using these API keys the location, i.e.~\emph{regionCode} in the API document, was set to the UK and~\emph{YVRP}s were crawled automatically. Yet, YouTube Data API v3 has some limitations as follows. First, one can crawl a limited number of~\emph{YVRP}s for each generated API key; the quota is based on the information crawled for each API request. Thus, it is important to crawl only sufficient information while using the YouTube Data API. For the detailed quota information, one can use the official page~\footnote{\url{https://developers.google.com/youtube/v3/determine_quota_cost}}. Second, the YouTube Data API v3 returns 50~\emph{YVRP}s in total for each API request; this is the maximum number that could be retrieved using the official API per request. Therefore, one needs to find a workaround to crawl more than 50~\emph{YVRP}s per query. For this purpose, the YouTube Data API v3 provides a field of~\emph{nextPageToken} that denotes a unique ID for the next page of the~\emph{YVRP}s of the current query. Then, this~\emph{nextPageToken} could be assigned to the~\emph{pageToken} field of the YouTube Data API v3 request while crawling the next page of the video search results of the same query.

Based on the aforementioned information, using the YouTube Data API v3 with the same educational query list in~\cite{gezici2022measuring}, in total 200~\emph{YVRP}s were crawled for each query. Similarly, in the scope of this work, since personalised search might complicate the bias analysis, the analysis was designed in unpersonalised search settings, i.e. there was no user information included in the API requests. The main reason of using automated crawling and annotation procedures in this paper is to investigate the source of bias in search results as well. Hence, 200~\emph{YVRP}s were obtained with the assumption that these search results could be the representative of the full video search results for the corresponding query that could be used to track the source of bias. Since crawling more~\emph{YVRP}s require huge processing time, especially in the annotation phase which uses deep learning-based automated models, 200~\emph{YVRP}s were selected for the source of bias analysis. To crawl the 200~\emph{YVRP}s for each query, four API requests were sent to the YouTube Data API v3 using the~\emph{nextPageToken} information. In this way, 200 results were obtained for all the educational queries, except the query of~\emph{Capital labour and power: Britain 1707-1939} in the NON-STEM major of~\emph{Politics}. Only for this query, YouTube Data API v3 returned 67~\emph{YVRP}s in total and changing punctuations in the query etc. did not change the retrieved search results by YouTube. Please note that the educational query list was not modified on purpose in order to obtain comparative evaluation results with our previous work~\cite{gezici2022measuring} and not to inject personal bias.

\begin{figure*}[!t]
  \centering
  \includegraphics[width=0.9\textwidth]{Annotation_Automatic_FlowChart.drawio.png}
  \caption{Flow-chart of the~\emph{Perceived} Gender Annotation}
  \label{fig:flowchart_gender_annotation_automatic}
\end{figure*}

\subsection{Annotation Procedure}
\label{sec:gender_bias_annotation_procedure}

The automated annotation procedure has two essential steps. First a video in a~\emph{YVRP} is annotated based on its relevancy with respect to the given query as \emph{relevant}, \emph{not-relevant}. If the video is \emph{relevant} to the given query, then the video is annotated with a probability distribution of \emph{male} and \emph{female} genders based on the~\emph{perceived} gender of narrator(s) through voice information instead of labelling each video with a single~\emph{perceived} gender label as fulfilled in~\cite{gezici2022measuring}. Note that before these two main annotation steps, videos with the following properties were discarded.

\begin{itemize}
    \item Short Videos: The videos that are shorter than 20 seconds.
    \item Unavailable Videos: The videos that has been removed by the user or from YouTube because of copyrights.
    \item Restricted Videos: The videos that require a user login to watch either because it is a private video or because of age restriction.
\end{itemize}

After discarding these videos, the first step of the annotation procedure was applied.

\subsubsection{Relevance Annotation}
\label{sec:gender_bias_dataset_automatic_annotation_relevance}

After the crawling, to automatically detect the relevance label of a given video with respect to the corresponding query, a document similarity approach was implemented.~\emph{YVRP}s were converted into textual contents which were then used to measure document similarity. The main idea here is to utilise the dataset in~\cite{gezici2022measuring} that had already been annotated with the relevance labels.
The approach of measuring document similarity combines the following three models to encode documents: Term-frequency muliplied by inverse document-frequency (tf*idf)~\footnote{\url{https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html}}, Universal Sentence Encoders (USE)~\cite{cer2018universal}~\footnote{\url{https://tfhub.dev/google/universal-sentence-encoder/4}}, and Sentence-BERT (SBERT)~\cite{reimers2019sentence} using SentenceTransformers~\footnote{\url{https://github.com/UKPLab/sentence-transformers/blob/master/index.rst}} Python framework. In addition to these, a jaccard similarity measure~\cite{niwattanakul2013using}, which is mainly based on the number of common words between two documents, was also implemented. Yet, the jaccard measure did not work well so it was discarded from the analysis.

\begin{table*}[!t]
\centering
\caption{Evaluation Results on VoxCeleb}
\begin{tabular}{ c|c|c|c|c}
 \hline
    Model & Gender & Precision & Recall & F1-score \\
 \hline
  \multirow{2}{*}{Feed-Forward Gender Detector} & Female & 0.79 & 0.79 & 0.79\\\cline{2-5}
 & Male & 0.87 & 0.87 & 0.87 \\
 \hline
 \multirow{2}{*}{inaSpeechSegmenter} & Female & 0.92 & 0.94 & 0.93\\\cline{2-5}
 & Male & 0.96 & 0.95 & 0.96 \\
 \hline
\end{tabular}
\label{tab:evaluation_result_voxceleb}
\end{table*}

After the implementation of these models, threshold values of each model were determined experimentally. For evaluating the capability of these models and tuning the threshold values on the textual contents of~\emph{YVRP}s, the dataset in~\cite{gezici2022measuring} was used as mentioned above. Then the results of the aforementioned three models of tf*idf, USE, and SBERT were merged for measuring document similarity. To automatically label the relevancy of the~\emph{YVRP}s that were crawled with the data crawling procedure as described in Section~\ref{sec:gender_bias_dataset_automatic}, these steps were fulfilled:

\begin{enumerate}[label=(\roman*)]
    \item Using the~\emph{videoID}, title, description, and subtitles of each video in the~\emph{YVRP}s of were crawled.
    \item Then, title, description and subtitles of each video were concatenated; each video was represented with this concatenated textual content.
    \item A preprocessing phase of removing numbers, punctuation, making lowercase, and lemmatisation was applied.
    \item The three models of tf*idf, USE, and SBERT were applied on the preprocessed textual contents of the~\emph{YVRP}s that were previously crawled and annotated in~\cite{gezici2022measuring}.
    \item Given the true relevancy label of a given document (video), experimentation was fulfilled with model representations of the given document and different threshold values for each model.
    \item The threshold values of 0.1, 0.5 and 0.5 were determined for tf*idf, USE, and SBERT respectively. If the computed document similarity score is below a threshold, then the document is labelled as not-relevant for the corresponding model.
\end{enumerate}

To automatically annotate a given video in the rest of the videos with a relevance label, three different models were deliberately used. After computing document similarity scores, these scores were reviewed as well. For the final decision about the relevancy of a given video, the document similarity scores of the three models were expected to be consistent. This means that a more conservative approach was taken, i.e. a given document/video is not-relevant if these three models agree, in order not to lose the relevant documents.

Based on the reviewing of the document similarity scores obtained from these three models, it was observed that the combined approach worked sufficiently well. The combined approach has the capability of detecting non-English as well as out-of-context videos. For instance, YouTube returned some videos related to Python context managers for the query/course module of~\emph{Management in context} in~\emph{Public Relations} major of NON-STEM field and the combined approach detected labelled those videos as~\emph{not-relevant}. Additionally, the combined model detected some non-English videos for the query/course module of~\emph{Urban sociology} in~\emph{Sociology} major of NON-STEM field. Note that in the scope of this paper, an additional relevance label of~\emph{N/A} was not used for non-English videos and videos without a narrator. Instead, non-English videos were labelled together with out-of-context videos as~\emph{not-relevant}. The videos with no narrator were handled during the~\emph{perceived} gender annotation in Section~\ref{sec:gender_bias_dataset_automatic_annotation_gender}.

\subsubsection{Perceived Gender Annotation}
\label{sec:gender_bias_dataset_automatic_annotation_gender}
As the second phase of the annotation procedure, if a given video is relevant then it was annotated automatically with~\emph{perceived} gender information by computing its probability distribution of male and female gender labels based on the audio of the given video. This phase is composed of two main steps. First, the given audio was classified into the segments of speech, music, or noise using the model of inaSpeechSegmenter~\cite{salmon2014effortless}. Note that the audio segment of music or noise correspond to no narrator case, i.e. this refers to the label of~\emph{N/A}. Subsequently, the inaSpeechSegmenter and Feed-Forward Gender Detector~\footnote{\url{https://github.com/x4nth055/gender-recognition-by-voice}} models were used independently to detect~\emph{perceived} gender on the speech segments. Finally, male (female) ratio was computed by measuring the time of the audio segment that is annotated with male (female)~\emph{perceived} gender label divided by the time of the full speech audio segment. The~\emph{perceived} gender annotation procedure is displayed in Figure~\ref{fig:flowchart_gender_annotation_automatic}.

inaSpeechSegmenter proposes a gender detection processing pipeline which is composed of three main parts. The first submodule of a Speech/Music segmenter based on Convolutional Neural Networds (CNN)~\footnote{\url{https://en.wikipedia.org/wiki/Convolutional_neural_network}} is responsible for discarding music and empty segments. Subsequently, features corresponding to speech segments are extracted using a common extraction framework. A simple energy threshold is utilised to discard frames with low energy. Lastly, Gaussian Mixture Models (GMMs), i-vectors, i.e. compact vector representation of speech utterance, and CNN systems are then leveraged to classify the remaining speech segments into male and female excerpts. The inaSpeechSegmenter has been trained on the INA's Speaker Dictionary~\cite{ddoukhanicassp2018} which contains about 32000 excerpts of 1780 male (94 hours) and 494 female (27 hours) speakers. This audiovisual corpus was annotated with a semi-automatic labelling protocol. For more details about inaSpeechSegmenter, please refer to the original paper~\cite{salmon2014effortless}.

The second model of the feed-forward gender detector is a deep feed-forward neural network of five hidden layers, i.e. 0.3 dropout rate after each dense layer, was presented in this tutorial~\footnote{\url{https://www.thepythoncode.com/article/gender-\\recognition-by-voice-using-tensorflow-in-python}}. The dataset that was used in the second model is Mozilla's Common Voice Dataset~\footnote{\url{https://www.kaggle.com/datasets/mozillaorg/common-voice}} which is a corpus of speech data read by users on the Common Voice Website~\footnote{\url{https://commonvoice.mozilla.org/fr}}. Before using the dataset, the dataset was balanced as the number of male samples equal to female samples, i.e. in total 67K samples with equal number of male/female samples, to prevent the model to favour one particular gender. In addition, for feature extraction this second model utilises Mel Spectogram~\footnote{\url{https://en.wikipedia.org/wiki/Mel_scale}} extraction technique to obtain a compact vector representation of length 128. For more details, please refer to the tutorial and the implementation of the model.

For evaluating the capability of the aforementioned two automated models, the dataset of VoxCeleb~\footnote{\url{https://www.robots.ox.ac.uk/~vgg/data/voxceleb/}} was used. VoxCeleb contains audio and video of short clip extracted from the interviews on YouTube. The dataset includes speakers from a wide range of ethnicities as well as ages. In terms of gender, the dataset is composed of 3682 male speakers (61\%), and 2311 female (39\%). Note that during the evaluation phase, only the audio clips were used since in the scope of this work, YouTube videos are annotated with probability distributions using only the audio information in a given video. Since the evaluation dataset is also from YouTube, model evaluation results for binary classification could be a good indicator in terms of the models' annotation capability. Based on the evaluation results shown in Table~\ref{tab:evaluation_result_voxceleb}, it is observed that inaSpeechSegmenter outperforms Feed-Forward Gender Detector by a large margin -- inaSpeechSegmenter gives aroung 20\% higher F1-scores for the female class, while around 10\% for the male class.

Note that the length of the educational videos crawled from YouTube might vary from couple of minutes to multiple hours. Hence, applying two deep learning-based models to get annotations might take a huge amount of time. To speed up the annotation procedure, rather than annotating the full video, a group of samples were selected from each video.
Moreover, most of these educational videos start with a musical intro and end with a musical outro. Thus, in order to make the sampling more focused on the video content, the sampling was initiated 10 seconds after the beginning and terminated 10 seconds before the ending. Then, for the sampling of each video, i.e. after discarding both the first and last 10 seconds, first 10 seconds of each video minute was taken as a sample. Finally, on each sample the annotation steps as denoted in Figure~\ref{fig:flowchart_gender_annotation_automatic} were executed. In this way, male/female probability distributions of each video were computed using the corresponding sample-level annotations of the aforementioned two models. Instead of~\emph{perceived} single labels, probability distributions are expected to provide smoother and more realistic bias results in comparison to the results in~\cite{gezici2022measuring}.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Free USA Media Mail shipping on orders of $50 or more with code 50SHIP on checkout!
Ahorn Books
Chose Commune
Here Press
Image Text Ithaca
KG Projects
Matte Editions
Palm* Studios
Skinnerboox
78 by Issei Suda
The story behind 78 tells of a promise kept after the passing of the Japanese master of photography Issei Suda, to publish a book with an original approach. Although the project originated in January 2019, 78 presents a selection of unseen photographs selected from the Suda archive in November 2019. Shot between 1971 and 1983, these photographs have never been published until this day.
In this book, Suda's unique and profoundly urban vision unfolds in a series of contrasts and allusions. The unexpected presence of animals and children's mischievous looks fill up the streets, adding a sense of strangeness to these everyday scenes. From Tokyo to its surrounding prefectures, the photographer's striking world is immersed in urban energy, bringing out - with a hint of humour - its spirit. The same energy can also be found in Suda's trademark deep and contrasted blacks, a unique hallmark of his work.
"It was only upon our return to France that I realised Suda had passed away at 78, the exact number of prints we had intuitively selected on that autumn afternoon. This book is a tribute to the great photographer he was." - Cécile Poimboeuf-Koizumi
French/ English / Japanese
© 2021, Citizen Editions Powered by Shopify

redpajama_set_name: RedPajamaCommonCrawl


text:
Tecoma fulva is a species of flowering plant native to South America. In the past, several species have been named, which are more recently considered to be subspecies.

 Tecoma fulva subsp. fulva
 Tecoma fulva subsp. altoandina J. R. I. Wood
 Tecoma fulva subsp. arequipensis (Sprague) J.R.I.Wood
 Tecoma fulva subsp. garrocha (Hieron.) J.R.I.Wood
 Tecoma fulva subsp. guarume (DC.) J.R.I.Wood
 Tecoma fulva subsp. tanaeciiflora (Kraenzl.) J.R.I.Wood

References

fulva
Flora of South America
Garden plants

redpajama_set_name: RedPajamaWikipedia


I have many clients that tell me that their internet "works" in most of the areas of the house and everything seems to be fine. I ask how much are you paying per month for your TV and internet and I get responses of $150-$200 per month. Every single month when we pay that money to the cable or satellite company we are paying as if we are getting the maximum internet speed and best TV experience. However after I test everything out, check the house wiring, check equipment, and see how everything is being used I often find that what you are paying for is not what you are getting. By using the right products, updated wiring, and proper network setup you can truly get what you pay for and create a more enjoyable experience.
We offer design, sales, and installation of Wi-Fi internet products. We also offer reliable wired and wireless network repair for your home or office. We have 25 years of experience serving clients in and around Orange County. Our goal is to make technology and smart home automation simple.


Buying a book is not the only way to read it. That's why there are public and municipal libraries all around Greece and of course all around the world. If you are a university student on a tight budget, as I was during my years at the university, you will find out that going to a good library to study and borrow books in the only way to finish your studies. You can now access the internet for information of course and you can actually find quite a lot of information on the subject you are working on, but even this new wealth of digital libraries in Greece doesn't always work.
In my personal case, the internet was still doing its first steps, about 20 years ago when I was studying, so more or less visiting libraries was the only way to go… Still the majority of books written have not been digitized. Commercially popular books are easier to be found of course, but older books and books related to sciences are more difficult or even impossible to be found.
In Greece nowadays, there are about 500 about big libraries found around the country and thousands smaller ones. The first indications of the existence of libraries in Greece are from the 6th century BC. The most well-known libraries of the ancient Greek world was the library of the philosopher Aristotle (4th century BC), the Library of Alexandria (3rd century BC) and Hadrian's Library (1st century BC). Libraries flourished during the Byzantine Empire, especially in monasteries.
Over the next four centuries, famous schools built their own libraries and some of them became very popular, because of the large number of books. Most modern libraries were founded in the 19th century, including the National Library of Greece, still the biggest library in Greece, that was established in the 1829. In recent years academic libraries have been developed in schools, universities and educational institutions, that follow all technological and scientific developments.
Unfortunately, the Internet is believed that it has negatively influenced the expansion of libraries during the last few decades. To be exact, I should better say that libraries in Greece and around the world have started to dwindle in numbers during that time, while they were still flourishing before the 90's. I know from my experience that at least 3 of the libraries I used to visit about 20 years ago when I was still studying have now closed and according to the Greek Statistical Authority, the number of libraries in Greece is at a constant downturn.
The only positive thing noted is that the volumes of books have been increasing constantly in the past years but the number of visitors to the libraries has severely been reduced and the same is true for the number of the books people borrow. I used to go to libraries almost daily 20 years ago, then I had a surgical procedure done and it's great, you should try it. Anyways, it's true that since I got my first university degree and my master degree that followed a couple of year after that, I stopped studying and doing research and no longer needed to visit these libraries were I used to go.
I still pay a visit to libraries but this is done very occasionally during the last years. In the past I can say that I only visited libraries for studying in Greece. Any time I wanted to just relax and read a book I would just buy that book and then keep it in my personal collection of books. I think most of the people did and still do the same thing. When it's about just doing research I think few buy and keep the books. They are mostly interested to get the information they want and don't really need to keep the books. That's why libraries are so useful.
Nowadays the youngest generations are very familiar with the internet, computers, tablets, mobile phones and technology in general. I think that they almost always use the Internet exclusively for studying even when they really shouldn't do that. This actually means that they more or less regard libraries as obsolete, but I believe that they are wrong.
Maybe in a few years from now digital libraries and the Internet can fully replace traditional libraries, as far as the availability of books is concerned, but I'm sure that this time has not come yet. I know because I am still doing research on various things from time to time, and most of the bibliography I am looking for cannot be easily found online. This means that you must either buy the books you need and read them, or borrow them from a big library.
Most young students, I believe, do not have the money to buy the books or just the mood to go and visit a library. They prefer to use the internet anyway but since the still do not have all the information they need the quality of their work is not as high as it used to be in the past. They either sometime understand the problem or they simply don't realize the problem.
I have a cousin that is a history professor, working for the University of Athens. She tells me that this is a real problem professors like her face all the time, and unfortunately it is getting worst year by year. The fact is that even though the University has many libraries that are open to all students for free, the majority of them still prefer to use the internet for their sources and studies, which is good to a certain point and very bad beyond that.
My cousin is very critical of this attitude and very often does not give good grades, but few students really change the way they work. She believes that this is a battle we are all losing and that the only way to get around this problem is to develop more digital libraries with as much published content being eventually digitized and added to them, because traditional libraries are dying.


NORFOLK — Virginia could look to so-called "sin taxes" on alcohol as a source of sustainable revenue which could also have the potential to curb overconsumption.
However, Old Dominion University researchers warn that these targeted tax hikes could temper growth in the burgeoning craft brewing and distilling industries.
In a chapter on sin taxes and alcohol consumption in the fourth annual State of the Commonwealth Report, researchers with ODU's Dragas Center for Economic Analysis and Policy examined how the tax on alcohol sales has proven to be a "markedly resilient" source of revenue.
Even during the Great Recession a decade ago, revenue from excise taxes on alcoholic beverages for state and local governments nationwide increased from $6.3 billion to $6.5 billion between 2007 and 2009.
Virginia's excise tax revenue reached $257 million in 2015, an important revenue source during a time of federal spending uncertainty.
Virginia's excise tax rates on beer, wine and distilled spirits all rank in the top half nationally – 21st in beer, tied (with Georgia) for sixth for wine and third highest for distilled spirits (in part because our state government directly controls liquor sales, one of 17 states to do so).
These relatively high excise tax rates result in Virginia having a combined average sales tax rate of 5.63, placing it 41st among states that have a sales tax.
Virginia, as an alcohol control board state, can also raise the markup on distilled spirits, increasing revenue without explicitly increasing excise taxes.
However, research about the society-wide negative effects of overconsumption has generally found that alcohol taxation across the United States and within Virginia is lower than optimal.
"That is, the tax revenue collected on alcohol does not appear to fully pay for the negative externalities imposed on society by way of its consumption," the State of the Commonwealth Report notes.
Therefore, an increase in "sin tax" on alcohol could provide a larger revenue stream for the state and local governments, while potentially reducing the risky behavior associated with overconsumption.
One potential negative byproduct of a tax increase is a price-point threat to Virginia's craft brewing and craft distilling industries.
While loyal customers of small, independent Virginia breweries and distilleries have not been motivated to switch brands because of modest price increases, "care must be taken not to throw these nascent and established firms out with the bathwater," the report notes.
Working to reduce onerous regulations on tasting rooms, especially for distilleries, is one way to encourage growth and revenue for the Commonwealth, the authors note.
The Dragas Center for Economic Analysis and Policy in the Strome College at Old Dominion University undertakes a wide range of economic, demographic, transportation and defense-oriented studies. Since 2000, the Center and its predecessors have produced the highly regarded State of the Region Report for Hampton Roads.
This year's full report is available at this link.


Thorgan Hazard missed a penalty but netted the decisive goal as Borussia Moenchengladbach battled to a 2-0 win over struggling Nuremberg to maintain their perfect home record and keep the heat on Bundesliga leaders Borussia Dortmund.
The win puts second-placed Gladbach six points off the top spot while Dortmund, who have a game in hand, face Fortuna Duesseldorf later on Tuesday.
Having picked up just two points on the road this season, Nuremberg headed to Borussia Park as rank outsiders against a Gladbach side who have now won all eight of their home games.
The underdogs almost sprang a surprise on five minutes when Alexander Fuchs hit the bar and goalkeeper Yann Sommer denied Adam Zrelak on the rebound.
Gladbach soon took control of the game but wasted two superb chances before half-time. On 39 minutes, Hazard's cross picked out Denis Zakaria a few metres in front of goal, only for the Swiss to fire his header straight at Tim Leibold on the line.
Hazard himself was left blushing moments later, after Gladbach were awarded a penalty for a foul on Florian Neuhaus. The Belgian attempted to chip his spot-kick down the middle, and watched on aghast as it floated over the bar.
His redemption came immediately after the break, as Gladbach counter-attacked and Hazard slotted in Ibrahima Traore's low cross at the far post.
Four minutes from time, the home side broke away again and Alassane Plea secured the three points with a coolly taken finish.


Jeanne Carlin explores a study on decision making in male carers of people with dementia and reflects on her own experiences in interpreting the findings.
Sarah Carr looks at a literature review assessing how choice is working for family carers in the context of social care personalisation.
Personal budgets in mental health: power to the people?
Professor Liz Hughes appraises and summarises a systematic review, which investigates the effectiveness of personal budgets for people with mental health problems.


Motorized window treatments are available in various power sources! Motorized window treatments are power driven and offer the ease of convenience, control and sophistication. Whether your windows have roller shades, automatic blinds, electric draperies, or roman shades, Royal Window Treatments has the knowledge and professional expertise to measure, fabricate and install your desired motorized window shades.
Battery Powered System – With this system, it's not required to run any wiring, the motorized shades operate on batteries and depending on usage, they would need to be replaced every 2-4 years. Battery powered systems are controlled with a remote control.
that requires wires to have reach from the motorized shade to the AC transformer. It's ultimately maintenance free and requires no batteries! Operation of DC powered systems is either by a wall switch or remote control and a pre-wiring plan is recommended.
on an AC voltage, from 120V-240V. Wires have to reach the motorized shade from a pre-wired AC resource. To operate an AC powered system it will require either a wall switch or remote control and a pre-wiring plan is recommended.
Radio Frequency System (RF) – A major advantage of radio frequency operated window treatments is that you don't have to aim the remote control at your window coverings (window shades, window blinds or window drapes) to operate them; you can operate them from virtually anywhere in the house!
Somfy® RF systems feature wireless controls that include: remote controls, wall switches, and programmable timers. Additionally window treatments that are operated by Somfy® RF systems can be integrated with an AV home automation system.
Infrared System (IR) – This is the fundamental system, which is not recommended. The remote control has to be pointed directly at IR receiver to operate the motorized window coverings individually. Therefore, your window treatments cannot have any obstructions in order to function…NOT recommended.
More Advanced Controls & Convenience – If your motorized window coverings are positioned in several rooms, and you would like your controller to operate from diverse locations, you might be required to operate several window treatments in chorus or independently. In this case, RF systems are HIGHLY recommended!
Hard Wired Wall Switches – For these types of controllers, an electrician is required to run wires from a power source to the position you prefer the wall switch to be, as well as to the window treatment's placement. A pre-wiring plan is recommended for hard wired wall switches.


text:
7 things you probably don't know about HIV
1. 36.7 million people are living with HIV globally
In 2016, an estimated 36.7 million people were living with HIV, including 1.8 million children. 19.4 million of these people live in East and Southern Africa.
2. 30% of those living with HIV don't know they have the virus
Since the beginning of the epidemic, more than 70 million people have been infected with the HIV virus and about 35 million people have died of HIV. The CDC recommends that everyone between the ages of 13 and 64 get tested for HIV at least once a year.
3. The only way to survive HIV is through treatment
People with HIV who are aware of their status can get HIV treatment (antiretroviral therapy, or ART) and live healthy, normal lives.
4. People with HIV that have undetectable viral load cannot transmit the virus
The amount of HIV in body fluids is called your viral load. Effective HIV treatment (antiretroviral therapy) suppresses the amount of HIV in body fluids to the point where it cannot be detected by the tests used in clinics.
When someone starts HIV treatment, viral load usually becomes undetectable within six months. The CDC has confirmed that a person with an undetectable viral load cannot transmit HIV.
Having an undetectable viral load does not mean you are cured of HIV. It does not mean you should stop taking treatment. It does mean that, as long as your viral load stays undetectable, you radically reduce your chance of passing HIV on to someone else.
5. Receptive anal intercourse carries the highest risk for HIV transmission than any other sexual behavior
According to the CDC, the risk of infection for receptive vaginal sex is 8 per 10,000 exposures. For insertive penile-vaginal sex, the risk of infection lowers to 4 out of 10,000 exposures.
Receptive anal intercourse with a partner who is HIV-positive is the sex act that's most likely to spread the virus. For every 10,000 instances of receptive anal intercourse with a partner who has HIV, the virus is likely to be transmitted 138 times!
Insertive anal intercourse poses a lower risk, with 11 infections per 10,000 exposures.
All forms of oral sex are considered low risk.
6. No one actually knows who was first to contract HIV
It had been widely thought that a Canadian flight attendant called Gaetan Dugas who died of AIDS-related infections in 1984 was responsible for the spread of AIDS in America.
But according to a new book, HIV was killing people in Africa long before then and the first case of infection could have been a hunter who became infected with blood from a chimpanzee that was carrying the disease as far back as 1908.
The hunter was wounded while tracking the chimp in south east Cameroon.
7. The Story of Ryan White
The first case of HIV infection to make global headlines was that of a 10 year old boy named Ryan White. He contracted the disease through a blood transfusion.
As a hemophiliac, Ryan became infected with HIV from a contaminated blood treatment and, when diagnosed in December 1984, was given six months to live.
Doctors said he posed no risk to other students, but AIDS was poorly understood by the general public at the time.
When Ryan tried to return to school, many parents and teachers in Howard County rallied against his attendance due to concerns of the disease spreading through bodily fluid transfer.
A lengthy administrative appeal process ensued, and news of the conflict turned Ryan into a popular celebrity and advocate for AIDS research and public education.
Surprising his doctors, Ryan White lived five years longer than predicted but died in April 1990, one month before his high school graduation.
www.cdc.gov
http://www.who.int
www.std-gov.org


Since the late 1980's EAM vendors throughout the world have pitched their products based partly on the ability to capture, manipulate, and analyze, historical failure data. Part of the stated benefits case is often the ability to highlight the causes for poor performing assets, provide the volume and quality of information for determining how best to manage the assets, and informing decisions regarding end-of-life and other investment points.
This benefits case covers the principal drivers for most maintenance managers today and it has been used to justify millions of dollars worth of investment, and has place the modern EAM system at the centre of corporations that are driving to improve asset performance. On the surface it appears to be a logical approach for problems relating to asset performance, and using this approach companies do, of course, achieve results.
The implementation of these products, when bought for these reasons, often focuses on optimizing processes to capture the dynamic data on asset failures, which is then used throughout the system. MRO style inventory management algorithms, for example, use this information as one of the key inputs to determine minimum stocking levels, reorder points and the corresponding reorder quantities.
If we want to understand the validity of this line of thinking it is necessary to first explore the aims of maintenance, and how asset data can be used to further those aims.
"Maintenance" is a term generally used to define the routine activities to sustain standards of performance throughout the in-service, or operational, part of the asset life cycle. In doing this, the maintenance policy designer needs to take account of a range of factors. These include the complexities of operating environment, the available resources for performing maintenance, and the ability of the asset to meet its current performance standards.
In the past, this would be the extent of the maintenance analysts' role. One of the realities they face is that at times assets are under a demand greater than, or extremely close to, their inherent capabilities. As a result analysts often find themselves recommending and analyzing activities of not only maintenance, but also other areas of asset management, namely those of asset modification and operations.
Safety and environmental compliance play their part in creating the drive for this activity, particularly given the changing legal and regulatory frameworks around these two areas; in some industries they are even the principal drivers. However for most businesses the goal remains that of maximum value from their investment. This means getting the maximum performance possible from the assets, for the least amount spent.
In the original report and appendices that produced Reliability-centered Maintenance (RCM) the authors defined critical failures, initially, as those failures with an impact on safety. Today the term "critical failure" is often used to group failures that will cause what companies consider to be high-impact consequences, a definition that is too variable for a general discussion. For the sake of simplicity, "critical failure" in this paper refers to all failures that will cause the asset to perform to a standard less than what is required of it.
So, in essence, the role of the policy designer can be defined as the formulating cost-effective asset management programs, routine activities and one-of procedural and design changes, to maintain standards of performance through reducing the likelihood of critical failures to an acceptable level, or eliminating then. This is also the essence of modern RCM.
Immediately we start to see a contradiction between the aims of maintenance, and those often quoted of EAM systems. Non-critical failures are those of low or negligible cost consequences only. These are acceptable and can be allowed to occur. Therefore a policy that focuses on data capture and later analysis as its base can be used effectively. Over time the level of information will accumulate to allow asset owners, and policy designers, to determine the correct maintenance policy with a high degree of confidence.
However, critical failures, those that cause an asset to under perform, have unacceptable consequences and cannot always be managed in a similar way. For example, if a failure has high operational impact or economic consequences, then allowing it to fail prior to determining how to manage them is actively counterproductive to the aims of cost effective asset management. Moreover, recent history reinforces the fact that failure of assets can lead to consequences in safety or breaches of environmental regulations.
So, if our policy for determining how best to manage physical assets is based around data capture, then we are creating an environment that runs counter to the principles of responsible asset stewardship in the 21st century.
The underlying theories of maintenance and that of reliability are based on the theory of probability and on the properties of distribution functions that have been found to occur frequently, and play a role in the prediction of survival characteristics.
Critical failures are, by their very nature, serious. When they occur they are often designed out, a replacement asset is installed, or some other initiative is put in place to ensure that they don't recur. As a result the volume of data available for analysis is often small, therefore the ability of statistical analysis to deliver results within a high level of confidence is questionable at best.
This fundamental fact of managing physical assets highlights two flaws with the case of capturing data for designing maintenance programs. First, collecting failure information for future decisions means managing the asset base in a way that runs counter to basic aims of modern maintenance management. Second, even if a company was to progress down this path, the nature of critical failures is such that they would not lend themselves to extensive statistical review.
By establishing an effective, or reliability centered, maintenance regime, the policy designer is in effect creating a management environment that attempts to reduce failure information, not increase it. The more effective a maintenance program is, the fewer critical failures will occur, and correspondingly less information will be available to the maintenance policy designer regarding operational failures. The more optimal a maintenance program is, the lower the volume of data there will be.
When maintenance policy designers begin to develop a management program they are almost always confronted with a lack of reliable data to base their judgments on. It has been the experience of the author that most companies start reliability initiatives using an information base that is made up of approximately 30% hard data, and 70% of knowledge and experience.
One of the leading reasons for this is the nature of critical failures and the response they provoke. However there are often other factors such as data capturing processes, consistency of the data, and the tendency to focus efforts in areas that are of little value to the design of maintenance policy. With EAM technologies changing continually, there are often upgrade projects, changeover projects, and other ways that data can become diluted.
There are still other key reasons why data from many EAM implementations are of limited value only. Principal among these is the fact that even with well-controlled and precise business processes for capturing data, some of the critical failures that will need to be managed may not yet have occurred. An EAM system, managing a maintenance program that is either reactive or unstructured, will only have a small impact on a policy development initiative.
The RCM Analyst needs to analyze all of the reasonably likely failure modes in these four areas, to an adequate level of detail. Determining the potential causes for failures in these areas, for a given operating environment, is in part informed by data, but the vast majority of the information will come from other sources.
Sources such as operators' logs are strong sources for potential signs of failure, as well as for failures often not found in the corporate EAM. Equipment manufacturers' guides are also powerful sources for gleaning information regarding failure causes and failure rates. However, all information from a manufacturer needs to be understood in the context of how you are using the asset, and the, often conservative, estimates of the manufacturer. For example, if there are operational reasons why your pumping system is subject to random foreign objects, for whatever reason, then failure rates for impeller wear can become skewed.
Other sources of empirical data can be found in operational systems such as SCADA or CITECT, commercial databanks, user groups, and at times consultant organizations. Similarly to information from manufacturers there is a need to understand how this applies to the operating environment of your assets. As asset owners require more and more technologically advanced products, items come onto the market with limited test data in operational installations, further complicating the issues of maintenance design through data.
The factors that decide the lengths that an RCM Analyst should go to collect empirical data is driven by a combination of the perceived risk, (probability x consequence), and of course the limitations set on maintenance policy design by commercial pressures. Even when all barriers are removed from the path of the RCM Analyst, they are often confronted faced with an absence of real operational data on critical failures.
The vast majority of the information regarding how the assets are managed, how they can fail, and how they should be managed, will come from the people who manage the assets on a day-to-day basis. Potential and historic failure modes, rate of failure, actual maintenance performed (not what the system says, but what is really done), why a certain task was put into place in the first place, and the operational practices and reasons why, are all elements of information that are not easily found in data, but in knowledge.
This is one of the overlooked side-benefits of applying the RCM process, that of capturing knowledge, not merely data. As the workforce continues to age, entry rates continue to fall in favor of other managerial areas, and as the workforce becomes more mobile, the RCM process, and the skills of trained RCM Analysts, provides a structured method to reduce the impact of diminishing experience.
Among the areas where modern EAM systems do provide substantial benefits is through driving out inefficiencies in business processes. Through the capture, storage, manipulation, and display of historical transactional data, companies can take great leaps forward in the level of efficiency with which they execute maintenance programs. For example, through ensuring that delays in executing work are captured analyzed and resolved, or by being able to display trends in performance and cost over time.
The effectiveness of a maintenance task comes from how it manages failure modes, not from the level of efficiency that it is executed with. The original RCM study revealed that many routine tasks could actually contribute to failure, or to lower cost-effectiveness by having limited or no impact on the performance of the asset. (In effect wasting the maintenance budget) Executing these tasks with greater efficiency would have either have no impact at all on effectiveness, or possibly even magnify the effects of unsuitable tasks.
For example, after a lot of time working with a utility company in the UK it became clear that the reported schedule compliance was not an accurate figure. Schedules were regularly coming in with 100% compliance, while the reality was that they were actually performing at around 25%.
After some investigation it turned out that the crafts people recognized the most of the regimes that were coming out of the system were either counter productive, or not applicable at all. So they were fortunately omitted. Prior to installing the EAM system they were working with job cards in separate systems, once the EAM went "live" these were collated and assigned to all similar assets regardless of operational context.
This is where RCM style methodologies contribute to the modern EAM or CMMS system. By providing the content that the system needs to manage, they are ensuring that the right job is being executed in the right way. This is common sense, and practitioners of RCM have been emphasizing this point for many years.
What is often not emphasized, however, is that having an effective maintenance program in place, and integrated with the EAM system ensures that future efforts of data capture are executed in a manner that supports the principles of responsible asset stewardship. The effect of building a data capture program on the back of an effective maintenance program is to reverse, over time, the ratio of hard-data to human-knowledge that is available for decision making.
On performing the analysis, the structured approach within the decision diagram drives RCM Analysts to develop an asset management program that is practical, cost effective, and tailored to a given level of performance and risk. There are two main outputs of any correctly performed analysis. The first is one-of changes to; procedures, software, asset configurations, asset types, company policies and asset designs.
The second area is a group of routine maintenance tasks designed to manage the failure mode under analysis. Aside from combinations of policies, RCM supports 5 different maintenance policy options as detailed below. These make up the bulk of the content that the EAM system is installed to manage.
Predictive Maintenance (PTive) – a task to predict when a failure mode is about to occur.
Preventive Restoration (PRes) – a task to prevent failure through applying a task, at a time or usage based interval, to restore the assets' original resistance to failure.
Preventive Replacement (PRep) – a task to prevent failure through replacing an asset or component, at a time or usage based interval.
Detective Maintenance – (DTive) a task to detect whether an item has failed or not. This task is only applied to failure modes that RCM classifies as hidden.
Run-to-Fail – (RTF) a policy to allow an asset to fail, rather than applying any form of routine maintenance. Failure modes that are allowed to run-to-failure have low, or negligible, consequences in terms of cost only. These are non-critical, or acceptable, failures that were referred to earlier in this document.
An RCM based process selects these tasks based on their applicability and effectiveness as defined within the decision algorithms. These issues have been commented on many times and will not be dealt with in great detail within this paper.
For modern RCM Analysts the routine maintenance tasks are of interest not only because of the impact they have on asset performance, but also because the way they can be used to develop the asset information portfolio, contribute to whole-of-life costing, and to provide an additional tool for proactive monitoring of asset performance and corporate risk exposure.
As with the logic of the decision diagram, the criteria and characteristics of each of these policy choices has been detailed many times, and it is not necessary to describe them in detail in this paper. However, it is necessary to detail how they affect the collection, management, and use of dynamic asset data.
As detailed in Figure 3 below, Predictive Maintenance (PTive) tasks are established to try to detect the warning signs that indicate the onset of failure, thus allowing for actions to be taken to avoid the failure. Yet there is also another aspect of PTive tasks that is often overlooked. That of the corrective, or Predicted (PTed), task once warning signs have been detected.
Immediately following the analysis, the information established at this point can be used for creating proactive whole-of-life costing models that are directly tied to performance and risk.
As time passes the amount of data that is collected on these tasks will grow, collected now in a responsible manner, and can also be used in statistical models regarding asset degradation and predictions of capital spend requirements. By the inclusion of these outputs of an RCM analysis, asset managers can use the results with increasing confidence as predictors of whole of life cost profiles, and end of life points.
Where Predictive Maintenance tasks cannot be applied, for whatever reason, the next two options on either side of the decision diagram are Preventive Maintenance tasks. These are tasks that are aimed at either restoring an assets resistance to failure (PRes), or replacing the asset at a time before the failures can occur. (PRep) Thus preventing failures. These tasks have limited use and are based on age, usage, or some other representation of time.
When applied correctly these tasks are part of the approach to maintenance that, by necessity, reduces the volume of failure data available for statistical analysis. However, with the component out of the operational environment, it can safely be tested to try to establish the extent of its remaining economically useful life.
This is an additional task and one that would not be generated from the RCM analysis. Yet it represents another aspect of responsible data capture and is an important element of businesses where confidence in statistical life prediction, and whole of life costing models, are of importance.
As with predictive maintenance tasks there are actually two tasks that are being implemented here. First the detective (DTive) maintenance task, and second the detected (DTed) maintenance task. The result of this is the same as with the predictive maintenance tasks. That is, it provides further information about the likely failure rate, collected in a responsible manner, which can be used to inform decisions regarding optimizing the frequency of this task.
Where n represents the number of times the DTive task is likely to be executed. This also drives estimates of the time between installation and likely failure. It needs to be recognized that the corrective, or DTed, task is executed at a time greater than end-of life due to the characteristics of this task. As time passes the data collected can be used to inform decisions and whole-of-life models with increasing certainty.
This is particularly relevant for hidden failures, or hidden functions as they are sometimes called. When implementing the outcomes of an RCM analysis, some of the tasks are DTive tasks. That is, they are tasks put in place to detect if a failure has occurred. Often, the items being tested have not been tested for a long period of time, sometimes years. And often nobody knows if they are working or not!
So when establishing the initial DTive task frequencies, often the information used is not very certain and backed by only the experiences and memories of those involved in the exercise. Fortunately manufacturers do often have a good level of information regarding failure rates in these sorts of devices. But the result is till quite conservative and not tuned for the specific operational climate. Performing DTive tasks will immediately help the company to establish some baseline information regarding failure rates of the device.
The last policy option, aside from redesign and combinations of tasks, is that of run-to-failure. This option is for the acceptable, or low / negligible cost, failures detailed in figure 1.The EAM will allow these failures to be captured for analysis to inform whole of life cost models, spending forecasts, and to be used in reviewing maintenance policies when relevant.
Along with the responsible data capture forced by these policy options, configuring and managing the EAM in line with RCM thinking will also allow visibility of exceptional failures.
Due to the way that RCM is, by necessity, carried out, there is the possibility that some failures may be missed. Modern methods of execution have expanded the original default method of team-based analyses to include expert analysis sources outside of the team, but there always remains the possibility that the analysis will miss a critical failure despite the best efforts of the analyst and those involved.
In these circumstances the data recorded in these exceptional failures will provide the impetus for the analyst to revisit the analysis to factor in this failure mode and to put in place a relevant management policy. It is not an area that is used for capturing data for statistical analysis and is, as the name suggests, the exception rather than the rule.
It can be seen that part of the role of the modern RCM Analyst is not only to minimize the volume of failure data that is collected for later analysis, but also to maximize the quality and usability of data that is captured via collection methods that support the principles of responsible asset stewardship. It can also be seen that advances in modern technology, combined with the growing needs of asset intensive companies, have enabled this information to be used in newer and more comprehensive ways than originally conceived of and correspondingly, not mentioned in previous work on RCM.
In particular it fuels the shift by the company away from the Static methods of life cycle costing, and towards the Proactive methods of whole-of-life costing. This is a step that enables companies to set up the data capture techniques and practices required to propel it towards the Stochastic, or probabilistic, model of whole of life costing.
Measurement is worth mentioning within this paper because the data that will be generated from applying an effective maintenance program will allow for companies to look further at how their program is functioning than they previously could. It is another example of the fundamental importance of effective maintenance policies.
These are perfectly reasonable measures and they give a company a snapshot of how a machine is performing to the standards that have been set for it. Regardless of how these measures are selected or generated, they are almost always lagging indicators. That is, they are indicators that tell you how your machine is performing after the fact.
Yet the data collected through establishing an effective maintenance program allows the company to generate a range of leading indicators. Measures that lead performance, or tell you that something is likely to begin to perform badly before it actually does.
The diagram in Figure 7 depicts the relative impact of these areas of leading indicators, and the smaller impact of performance measures established in the traditional lagging approaches. These are the key areas of the RCM Scorecard, a tool first published in the book, "The Maintenance Scorecard", and the subject of a separate article in this area.
Are the tasks actually predicting or preventing failures?
The actual measures contained within The RCM Scorecard are detailed fully within the book. It provides, arguably, a stronger level of benefit to a company than direct measures because it allows them to tap into the results of mainly leading indicators, thus heading off poor performance before it appears on the management report. Regardless of the actual measures used, the point remains that this is only possible due to the creation, in the first instance, of the effective maintenance program.
We began this paper discussing the three key drivers of maintenance that EAM systems often target. Without considering the different operating environments of different companies, these do cover the basic drivers of most maintenance departments.
To develop a maintenance policy designed to minimize the total cost of managing and operating the assets throughout its entire life cycle for a given level of performance and risk.
Obtain maximum efficiency out of the resources used to carry out the maintenance policy, driving unit costs further towards the optimum level.
To steadily build the asset data portfolio to allow future decisions regarding the asset base with increasing levels of confidence.
It could be argued that methods based in RCM style thinking alone could satisfy all three of these primary drivers of maintenance management. But the business processes that would be required to do so would be onerous and would restrict the ability of the company to manipulate and analyze data effectively. As well as being a burden to those trying to manage the maintenance workload.
It could also be argued that implementing EAM or CMMS, without implementing a parallel, or leading, initiative to create an effective maintenance policies will produce limited results, possibly exacerbate the current situation by allowing the company to perform incorrect work efficiently. And potentially create an environment where the assets are being managed in a way that is contrary to principles of responsible asset stewardship.
This line of thinking can lead to only one conclusion. The development of effective maintenance policies is the foremost consideration for modern asset managers. When done correctly it provides the base for business processes, inventory management techniques and methods, software configurations and selection, and the numbers and skill requirements of labor.
Aside from these tactical advantages, it also offers the strategic advantages of improving the whole-of-life management and understanding of the physical asset base, and the way that it is monitored and managed through performance measures. However, once the program is created, attempting to manage the asset base without leveraging off the advances in modern maintenance software deprives the organization of a tremendous opportunity for improvement.
This viewpoint is not new, nor is it particularly complex. It is a common sense approach and is an extension of the basic way that maintenance managers acted prior to discovering technology and being drawn down the path of increasing functionality, graphing, mobile devices and other gadgetry. It just seems to have been lost in the maze of tools that we are faced with today!
Of particular importance in this paper is the growing role of the RCM Analyst. Once a separated facilitator or a sole analyst, the RCM Analyst is a role that is by necessity becoming a lot broader. Covering a range of additional areas of expertise. A 20th century facilitator was generally driven to apply a team-based method and to complete the analyses. A 21st century Analyst is generally the owner of the program for their area or region. They are responsible for its upkeep, implementation, for ensuring that it is effective, for establishing the links to whole-of-life costing, and for capturing the knowledge of the organization through the application of the method in a flexible fashion. A new role for a new set of challenges!
About the Author: Daryl Mather is a specialist in the areas of physical asset management. He is the author of the book "The Maintenance Scorecard" and currently works with selected clients on a global basis.
MRO stands for Maintain, Repair, and Operate and is an acronym widely used within the EAM/ERP industry and associated with inventory management from an asset perspective rather than from a production perspective. The difference is that with ERP style inventory management the focus is on "just-in-time" methods. While MRO style inventory management focuses on "just-in-case", or probabilistic methods.
The author acknowledges that the definition of what is an acceptable, or unacceptable, standard of performance is an extremely complicated area and one that would take several articles to cover in adequate detail.
The Iowa Division of Labor Services, Occupational health and Safety Bureau, issued a citation and notification of penalty to Cargill Meat Solutions, on the 30th of January of 2006. This citation and notification or penalty required corrective actions such as the establishment of a preventive maintenance program and training of maintenance personnel on potential failure recognition among a range of initiatives to be implemented. This is just one of a number of recent safety events where maintenance has been flagged as a contributing factor.
Anecdotal information provided to the author from senior management within a range of companies in the water industry of the United Kingdom places asset failures as being responsible for approximately 40 – 60 % of breaches of consent. In this context "consent," relates to guidelines designed to protect the environment to an agreed level. In infrastructure this is thought to be even higher. This particular industry represents one of the world's first water networks and much of the infrastructure is ancient.
GEM stands for generic error modeling and was first developed by Professor Rasmussen of MIT following his review of the incidents leading up to the three-mile island disaster in the USA. The field of human error is a fundamental area of modern reliability management and has been advanced greatly by the works of James Reason, of Manchester Business University in the United Kingdom.
The strategy options, or policy options, offered within RCM are detailed in the RCM standard SAE JA1011.
This could theoretically, be suitable for all companies that need to manage physical assets. However it has particularly importance for financially regulated institutions and companies that need to prove the case for funding.


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"There they are Albert....faces of stone"
Female Filmmaker Project
Female Filmmaker Project: Ratcatcher (Lynne Ramsay, 1999)
Lynne Ramsay is a visual poet, more concerned with the meaning behind her images than the machinations of plot. In her work she evokes specific moods with colour palettes or lingering imagery.We Need To Talk About Kevin would douse itself in reds to symbolize a tying together of childbirth and violence. Morvern Callar focuses on textures, beauty in hyperfocused imagery of Christmas Trees and Club lighting as well as others. Ratcatcher is hazy, filled with browns and death seeps out of the sides of the picture. It is ironic that Ramsay infuses normal life giving or transitional narrative modes (Childbirth in Kevin, Adolescence in Ratcatcher) with her preoccupations of decay or destruction, but they exist in her work (Callar deals with suicide), and she is first and foremost a visual filmmaker.
The film begins with the death of a 12 year old boy in an accidental drowning due to play fighting. Ramsay uses close up shots of his hands, and the reaction of a mother who fears it is her own child through a window to convey this loss, but even more than the blunt imagery of a pale ghostly hand she created a world of rigor mortis in Glasgow. The air seems thick with poison, and the world is depleted of it's colour. Rats infest the streets as garbage workers are on strike and the unwanted items of houses get tossed out to fill up the streets. And it is a world of seemingly endless sorrow with it's lack of anything resembling vibrancy. Glasgow is in a transitional place in this picture with many occupants being moved around slowly into better housing, and garbage men are fighting for more significant rights to better their own lives. It mirrors James' growing up from boy to teenager. Everything should be progressing for James and Glasgow, but Ramsay-to the film's strength- is more interested in the current harsh conditions of the area and the guilt of having been involved in his friends death than a feel good narrative of improvement.
James isn't the only character struggling to make it in Glasgow. He befriends a girl (Margaret Anne) early in the picture who does sexual favours on the side, but is mercilessly mocked for this reason, her weight, and her need to wear glasses. In their relationship the film finds some of it's few happier moments. James and Margaret Anne share many significant scenes and seem to have an instant bond. She finds him more trustworthy than the boys he sometimes runs around with (as he treats her with common respect), and he doesn't have to put up his guard and act hypermasculine to impress her. He even attempts to retrieve her glasses from a pond at one point despite his post traumatic fear of water. He has a similar relationship with his sister, and while they fight as siblings do they shelter each other from the harshness of their sometimes violent father. The humanity and character interaction of James with the various women in his life are often the films sweetest moments. The presence of the gang of boys his age who are more interested in violence, teasing and making it with girls seems foreign to him after the events of the drowning earlier in the picture. His greatest betrayal growing up is even when one of his few male friends, who had a fondness for animals, begins to exhibit violent behaviour to those creates he once loved ,and James finds his friendship with him crumbling. In these ways Ratcatcher exhibits traditional ideas of masculinity as being toxic to male development, and it backs this up with the abusive relationship he has with his father whose only interest for James is to become interested in sports despite his hatred of Football.
James does find solace though, and Ramsay gives him these happier moments by taking him to a safe zone. It would be his future housing, and doubly a look at the future Glasgow has in mind for it's poorer residents. In these moments James explores, he loses himself in a field, and the shooting style changes from one of decay to life. The sun seems to arrive for the first time in the picture, and colour finally fills up the frame with deeper blues and rose-y golden fields. He's like a kid here, not worrying about the future problems he'd find in adolescence and adulthood. He seems to have a way out in this luxurious house (at least compared to his old one), but the films final scenes are worrying. In a moment of inevitability he plunges himself in water and he begins to suffer the same fate as his friend. If this is to be read as metaphor then there was no way out of growing up for James, and his housing situation wouldn't take away the grief that came from his maturing, and for Glasgow improving housing would only be a temporary fix to a rat's paradise.
Posted by Willow Maclay at 7:01 AM
Labels: Female Filmmaker Project, Gender, Lynne Ramsay, Ratcatcher, Women Directors, Women in Cinema
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