Teacher Perceptions of the Value of their Unions in their Daily Work During the COVID-19 Pandemic
Bibliographic record
Abstract
This study focuses on the perceptions of Toronto District School Board teachers regarding the value of their local union during the COVID-19 pandemic. Examining this period of heightened tension and change to teachers’ work provides insight into a unique moment in history and serves as a reflective tool for union and school leaders. Considering the intersection of teacher contexts, teacher perceptions, and the external pressures of the broader education landscape, I explore the impact of the pandemic on teachers’ work and how that relates to teacher relationships with their local union. This qualitative study involved 21 interviews with full-time contract teachers at the Toronto District School Board. The study covered the period from September 2019 to the end of the school year, June 2022. The pandemic added a layer of context that complicates teacher work and, findings suggest, teacher perceptions of the union’s ability to impact that work. Overall, the experience of the pandemic found teachers isolated from one another, with a work intensification that many found overwhelming. They perceived that their unions were not advocating or communicating at a time they needed them the most. Was this a missed opportunity for member union engagement during a tumultuous time of heightened emotion and conflict or more indicative of the limited power of unions in situations not directly covered by a collective agreement? A growing sense of dissatisfaction with the union and desire for change resulted in an almost complete change of Elementary Teachers of Toronto leadership in spring 2022.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".