Generalized harassment in Canadian universities: policies and practices addressing bullying in the academic workplace
Bibliographic record
Abstract
This thesis explores the implications of anti-harassment policies at Canadian medical-doctoral universities. The problem of generalized harassment as a phenomenon of academic bullying is identified and defined. This thesis explores how anti-harassment policies and practices of Canadian medical-doctoral universities have come to be, as well as their implications for academics. Chapter one identifies the methodology of the thesis, a comparative policy analysis of the policies and practices of Canada's medical-doctoral universities. Chapter two describes the theoretical foundations used in the thesis: theories of academic organizational control, policy formation, problem representation, and manifest and latent functions. Chapter three reviews contemporary literature on the role of universities in society and the phenomenon of generalized harassment in academia. Chapter four reports the results of a comparative analysis of the anti-harassment policies and practices of Canada's medical-doctoral universities, which reveal three approaches to anti-harassment policy. Chapter five links the theoretical to the empirical in order to better understand the phenomenon of generalized harassment in Canadian medical-doctoral universities, and the implications policies and practices have for the future of collegiality.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".