Exploring the Potential of Data Collected Under the Federal Contractors Programme to Construct a National Picture of Visible Minority and Aboriginal Faculty in Canadian Universities
Bibliographic record
Abstract
Based on data collected for the Federal Contractors Compliance Program contained on university websites, this paper attempts to make some informed and accurate assessments of the representation of racialized and Aboriginal faculty. Its preliminary findings reveal that there are significant variations among universities in the percentage of visible minorities and Aboriginal faculty; that there is a relationship between Employment Equity policies and higher percentages of visible minorities and Aboriginal faculty; that the expectation that “visible minorities” and Aboriginal faculty would be over-represented among contract faculty does not hold for “visible minorities” and that racialized and Aboriginal faculty tend to be clustered in certain faculties. Further research and more disaggregated data is, however, required to confirm these initial findings. À partir des données collectées pour le Programme de conformité des contrats fédéraux offert sur les sites Internet universitaires, nous tentons dans cet article d’établir de manière éclairée et exacte la présence d’un corps professoral racialisé et autochtone. Nos premiers résultats révèlent des variations importantes parmi les diverses universités en ce qui concerne le pourcentage des minorités visibles et des Premières nations dans leur faculté, la relation entre les politiques d’équité de l’emploi et une proportion plus élevée des unes et des autres, une conjecture que toutes deux seraient surreprésentées au sein des professeurs sous contrat qui ne tient pas pour les dites «minorités visibles», mais qui est confirmée quant à un corps professoral racialisé et autochtone tendant à être regroupé dans certaines facultés. Il faudrait cependant une recherche plus avancée et plus de données désagrégées avant de pouvoir confirmer ces résultats préliminaires.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".