Education for education workers: CUPE Local 3906 and the expansion of academic training
Bibliographic record
Abstract
This chapter focuses on training for academic workers from an employment-centric lens and examines the role that trade unions, such as the Canadian Union of Public Employees (CUPE) Local 3906 Unit 1 (representing primarily teaching assistants), play in advocating for training for academic workers at McMaster University. While many other contributions in this volume approach training from a pedagogical lens and examine the role that training plays in cultivating an environment where innovative approaches are developed and teaching and learning are properly valued, this discussion examines training primarily in the context of an employer-employee relationship though a lens of collective bargaining. In recent rounds of collective bargaining, most notably for teaching assistants in 2019, CUPE tabled and secured language to implement a comprehensive, mandatory, and paid training program. This represents the most meaningful inclusion of pedagogical training in collective agreements bargained by Local 3906 since teaching assistants at McMaster first unionized in 1979. This article seeks to contextualize and explain how collective bargaining has facilitated increased awareness of, and access to, pedagogical training for thousands of academic workers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".