Learning to Teach, Learning to Endure: Teacher Candidates’ Experiences of Sexual Harassment in the Ontario Teaching Practicum
Bibliographic record
Abstract
Despite increased awareness of workplace sexual harassment engendered by the #MeToo movement, and the emergence of research on sexual harassment in experiential learning placements, there is no substantial published research on teacher candidates’ (TCs’) experiences of sexual harassment (SH) during mandatory school practicum. TCs are particularly vulnerable due to their typically young age, non-unionized position, inexperience within the educational system and most workplaces, and unequal power relationships with associate teachers (ATs): senior colleagues who supervise and evaluate a TC’s practicum. Currently, teacher education programs may lack the resources and supports required to address TCs’ experiences of practicum SH; this may jeopardize TCs’ mental health, self-efficacy, and success at a critical point in their lives and careers. This thesis investigates how TCs who faced sexual harassment during practicum experienced its impacts, the reporting process (if applicable), and institutional responses, guided by the following research questions: 1) What are the impacts of SH on teacher candidates? 2) Which factors contribute to a TC’s decision to report – or not – SH experienced during practicum? 3) How do different institutional responses to reported SH impact TCs? Findings were translated into recommendations to faculties of education to improve SH prevention during practicum and develop supportive response procedures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".