Ideological Orientation of Professors and Equity Policies for Racialized Minorities
Bibliographic record
Abstract
This paper examines the extent to which university professors in Canada are supportive of policies that set targets for admission to colleges and universities and that help increase employment opportunities for racial and ethnic minorities in the wider market. Specifically, we tested the hypothesis that support for equity legislation is primarily accounted for by professors' ideological orientation and less so by their collective or self-interest. Using a large study conducted in 2001, we showed that the most important explanation for support for race-directed equity legislation is rooted in the perception of discrimination and a left-oriented ideology which includes egalitarianism, union support, and strike militancy. As well, the analysis revealed that racialized minorities are more, and higher income individuals are less, supportive of race-targeted equity policies. The policy implication of results is discussed. Dans cet article, nous recherchons à quel point les professeurs d'université au Canada soutiennent des politiques d'objectifs à atteindre concernant l'admission de minorités raciales et ethniques dans les institutions post-secondaires afin de les aider à obtenir plus de possibilités d'emploi dans le marché en général. Plus précisément, nous avons testé l'hypothèse selon laquelle c'est surtout l'orientation idéologique des professeurs qui les mène à tenir compte d'un soutien à une législation sur l'équité, plutôt que leur intérêt collectif ou personnel. À partir d'une grande étude conduite en 2001, nous montrons que l'explication la plus fréquente pour soutenir une telle législation qui vise la question raciale, est ancrée dans la perception d'une discrimination et d'une idéologie de gauche qui inclut l'égalitarisme, le soutien aux syndicats et un militantisme pro-grèves. De même, notre analyse révèle que les minorités racialisées sont plus en faveur de politiques d'équité envers les races, alors que les personnes à revenus plus élevés le sont moins. Les implications de ces politiques font l'objet d'un débat.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".