United Steelworkers Local 1998 Employee Experiences During COVID-19
Bibliographic record
Abstract
Universities serve as major employers in Canada and around the world. In Toronto, the University of Toronto is the city's largest employer, yet employee experiences are not well understood. In the context of the COVID-19 pandemic, workplaces such as the University have had to adjust to public health guidelines and take measures to ensure their employees' safety. This paper explores the experiences of administrative and technical employees at the University of Toronto represented by the United Steelworkers (USW) Local 1998. Twenty-one employees were recruited and interviewed over zoom between July and November 2020. Key findings include (1) benefits and disadvantages of working from home, (2) desire for some continued work-from-home post-pandemic, (3) accommodation issues, (4) concerns over long-term job security, (5) changing worker relations and implications, (6) precarity for casual staff, and (7) employee aspirations and pessimism for improved working conditions. These findings point to several important implications for policy and program development and union organizing, including the need to develop flexible work cultures and guidelines for managers during and post-pandemic, and the need for accommodations and compensation for home-office equipment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".