Tuition rebates and the teaching support staff union: An examination of the textual coordination of university bargaining
Bibliographic record
Abstract
For the past decade unionized teaching assistants in Canada have secured tuition rebates through collective bargaining. Graduate student bargaining is rooted in public sector regulation, social organization, and state formation. I examine how ruling relations coordinate teaching assistants’ inability to secure a tuition waiver in the negotiation of the Teaching Support Staff Union’s latest contract which is dated from 2004-2010. In this thesis, I examine two texts that were influential in bargaining. First, I look at a Labour Relations Board decision over a TA strike at the University of British Columbia. Second, I will examine a leaked document from the Public Sector Employer’s Council dictating the bargain process to the University. Dorothy Smith’s “Institutional Ethnography” and “Mapping the Social Relations of Struggle” as methods of inquiry allow me to see how texts are coordinated in University bargaining and what teaching assistants can do to resist them.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.021 | 0.034 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".