Strategies of Engagement: How Racialized Faculty Negotiate the University System
Bibliographic record
Abstract
The demands of the academic profession and the ways that universities are increasingly shaped by the neoliberal ideologies of competitiveness, individualism, and conformity, influence how racialized faculty perceive their experiences within universities and position themselves to effectively navigate and/or resist the assimilating terrain. The findings from an analysis of approximately eighty-nine interviews with racialized faculty members in universities across Canada indicate that racialized faculty members employ three strategic tendencies—compliance, pragmatism, and critical participation—to maintain their presence in their universities and assert their role as professors, and, in so doing, conform to, resist, and/or transform the institution. Les exigences de la carrière universitaire et l’orientation des universités de plus en plus marquée par les idéologies libérales de compétitivité, d’individualisme et de conformité, influencent la manière dont le corps professoral racialisé perçoit son expérience au sein de son institution et se positionne afin de louvoyer avec quelque efficacité et/ou de résister à ce terrain assimilateur. Les résultats de l’analyse d’environ quatre-vingt-neuf entrevues avec ses membres partout au Canada indiquent qu’ils suivent stratégiquement trois tendances – soumission, pragmatisme et participation critique – pour maintenir leur présence dans leurs établissements et affirmer leur rôle de professeurs ainsi que, ce faisant, se conformer à l’institution, y résister et/ou la transformer.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.038 | 0.023 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".