Bargaining for Contract Academic Staff at English Canadian Universities
Bibliographic record
Abstract
Successful unionization of, and conclusion of collective agreements for, contract academic staff in English Canada challenges the received wisdom that the Wagner Act model is an insurmountable obstacle to the unionization of contingent labour.It provides an example that might prove instructive for other contingent workers.This paper describes the process of unionization of contract academic staff in English Canada and seeks to explain its relative success.We suggest that the exceptional situation of contract academic staff as non-unionized workers in an otherwise unionized environment, access to the expertise and resources of large, national unions or associations and a sophisticated national strategy were contributing factors to successful unionization.The paper also considers the degree to which contract academic staff collective agreements fulfill or fall short of the promise of unionization.We analyze sample collective agreements, noting the variety and strength of various contractual models.We conclude by suggesting that contract academic staff have benefitted considerably from unionization.Despite these successes, the experience of contract academic staff supports critiques of the Wagner Act model as applied to contingent labour.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.045 | 0.007 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 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".