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Record W2073257374 · doi:10.1353/ces.2012.0008

The Representation of Racialized Faculty at Selected Canadian Universities

2012· article· en· W2073257374 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian ethnic studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCritical and Liberation Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentation (politics)SociologyPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

Gathering accurate data on representation of racialized and Indigenous faculty is compounded by the lack of reliable, detailed data. The paper attempts to redress the lack of data, based on a methodology of facial and name recognition in a small sample of Canadian universities. Photos and names of racialized faculty on departmental websites at selected universities in three faculties: engineering, management/business, and social sciences were examined. Where photos were not provided, links to individual faculty websites, research websites and departmental profile pages were examined. Where “visible minority” data were available, intersecting markers of identity such as gender, ethnicity, national identities, class and age are not evident. The average percentage of racialized faculty in this sample of ten Canadian universities is 12.3 percent. While it is difficult to identify baseline numbers for comparative purposes, the overall percentage of racialized faculty in Canadian universities is 15.9 percent. Chinese, South Asian, Arab and Black were the main ethnicities. In a sample of five universities surveyed, faculties of engineering and business accounted for more than half of the total racialized faculty at the university; in another two universities, the percentage is nearly half, and in the university with the smallest proportion of racialized faculty in engineering and business, the percentage is still more than one quarter of the total. At this point in the data collection, size of university does not appear to make much difference, although we await results of a larger sample. The two smaller institutions have lower proportions of racialized faculty. Among those who have somewhat larger proportions of racialized faculty, one is large and the other medium in size. The location of the university likely has a more profound effect, by size of city and by province, but we do not have sufficient data at this stage to make a case for location. Our figures also demonstrate that the lumping together of at least eleven ethno-racial groups into the category of “visible minority” in the Census data does a serious disservice to the understanding of ethno-racial positioning the Canadian universities. La collecte de données exactes sur les minorités visibles et les autochtones représentés au sein du corps professoral est compliquée par le manque d’informations fiables et détaillées. Dans cet article, nous tentons de compenser cette insuffisance à partir d’une méthodologie de reconnaissance du visage et du nom dans un petit échantillon d’universités canadiennes. Nous avons étudié les photos et les noms de membres de la faculté racialisés sur les sites web de celles que nous avons sélectionnées, et ce dans trois départements : Ingénierie, Gestions d’entreprise/Commerce et Sciences sociales. Lorsqu’il n’y avait pas de photos, nous sommes allées sur les pages de profil et les sites web de ces départements, ainsi que sur ceux de la recherche. Lorsque les données «minorités visibles» étaient accessibles, les marqueurs d’identification qui se recoupent, tels que le genre, l’ethnicité, les identités nationales, la classe et l’âge, n’étaient pas évidents. Le taux moyen du corps professoral racialisé dans cet échantillon de dix universités canadiennes est de 12,3 p. cent. Quoiqu’il soit difficile d’établir des chiffres de base à des fins de comparaison, ce pourcentage dans ce type d’établissement au Canada en général est de 15,9 p. cent. Les principales ethnies représentées sont les Chinois, les Sud-Asiatiques, les Arabes et les Noirs. Dans cinq des institutions soumises à notre enquête, les départements de l’Ingénierie et du Commerce comptaient plus de la moitié des professeurs de minorités ethniques de leur université; dans cinq autres, ils en comptaient presque la moitié, et dans celle qui en avait le plus petit nombre, celui-ci faisait encore plus d’un quart du total. À ce niveau de la collecte de données, la taille de l’établissement ne semble pas faire beaucoup de différence. Nous attendons cependant les résultats d’un échantillon plus large. La proportion du corps professoral racialisé est plus faible dans les deux universités les plus petites. Parmi celles où ce taux est quelque peu supérieur, il y en a une grande et une moyenne. Les conditions locales de l’institution, soit la taille de la ville ou la province où elle est implantée, ont probablement un impact plus profond, mais nous n’avons pas assez de matériel à cette étape de la recherche pour justifier le rôle de l’emplacement. Nos chiffres montrent aussi que le fait d’amalgamer au moins onze groupes ethno-raciaux dans une seule catégorie de «minorités visibles» dans les données du recensement, contrecarre sérieusement l’effort de comprendre la situation des universités canadiennes sur le plan ethno-racial.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.253
GPT teacher head0.475
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it