Students' Perceptions of Fairness Following an Academic Strike
Bibliographic record
Abstract
Rising rates of unionization in university settings suggest that campus labour \ndisputes are likely to become an increasingly relevant issue. The research \nquestion in the current analysis asked which factors contributed to students’ \nperception of fair treatment following a university labour disruption. A longitudinal \nsurvey of students’ experiences was conducted before, during, and \nfollowing a 12-week strike by teaching assistants and contract faculty at a large \nCanadian university. Hierarchical regression analysis revealed that students’ \npre-strike satisfaction with their academic program contributed to a perception \nof post-strike fairness. The more students’ plans had been affected by the \nstrike, the greater the reduction in perceived fairness. Post-strike fairness increased \nsignificantly the more students were satisfied with course remediation \nand the more they felt they had a faculty member to turn to following the strike. \nInterestingly, neither students’ levels of financial concern, nor their attitudes \ntoward the strike, predicted perceived fairness. Implications for addressing \nstudents’ concerns in the wake of an academic labour dispute are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".