Bibliographic record
Abstract
This chapter explores challenges to maintaining strong bargaining units posed by threats of attrition, either through formal decertification or by other means producing similar outcomes. It first documents trends in numerical attrition at Sidhu & Sons, in the context of Canada's introduction of other more highly deregulated temporary migrant work programs (TMWPs) operating in agriculture and those programs' subsequent growth. The size of the bargaining unit at Sidhu, comprised of SAWP employees exclusively, shrank after certain employees' attempt to decertify it, despite the fact that the Labour Relations Board (LRB) had refused to cancel its certification. Given the absence of an active attempt to decertify the bargaining unit, it is nevertheless difficult to determine how attrition continued at Sidhu. To demonstrate the how of this often subtle modality of deportability, the chapter then chronicles strategies fostering attrition in the bargaining unit encompassing SAWP employees at Floralia Plant Growers Ltd., which the union originally tried to draw into the foregoing complaint of unfair labor practices and coercion and intimidation directed at Sidhu.
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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 | 0.007 | 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 | 0.008 | 0.011 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".