Engendering inequities: precariously employed academic women’s experiences of student evaluations of teaching
Bibliographic record
Abstract
Grounded in intersectional feminist approaches, this study explores the equity impacts of student evaluations of teaching (SETs) on precariously employed women in the academy. Despite their overrepresentation in the academic teaching workforce, precariously employed women are a demographic group that remains underrepresented in research on SETs. Thirty-four qualitative interviews with precariously employed academic women at a university in Ontario, Canada, were conducted to explore their experiences of SETs. The participants critiqued SETs’ role in perpetuating feminized and racialized labour market precarity, and undermining their professional autonomy and professionalization. They also described how SETs subject them to discriminatory evaluations based on their gender, race and age, and the impacts thereof on their workload and mental health. This study’s findings reveal the importance of recognizing SETs’ impact on equity and the need to change teaching evaluation policy in higher education.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| 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".