Negotiating job security and capital investments in response to deindustrialization: the case of Canada’s auto sector
Bibliographic record
Abstract
Job security has always been a paramount concern for the trade union movement. This article explores the ways that unions used collective bargaining to gain a measure of job security for their members in the face of deindustrialization as unionized factories in North America began to close in large numbers after the 1970s. These new measures included advance notice, severance pay, plant closing moratoria, restrictions placed on plant movements, transfer rights, and expanding the scope of collective ‘social’ bargaining to cover training and adjustment. In some sectors, such as automotive, collective bargaining has also been extended into areas normally left to management. The price was often high. Eventually some unions, notably the Canadian Auto Workers (established 1985; part of Unifor after 2013), prioritized winning new capital investments and product lines for unionized plants in their negotiations, though often at the cost of jobs, wage freezes or reductions, and other concessions. By focusing upon auto sector deindustrialization in Canada since the 1980s, we draw lessons from more recent union bargaining strategies, and how they constitute an important element of worker responses to industrial job loss and manufacturing closure.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.043 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".