“We Simply Have to Do that Stuff for our Survival”: Labour, Firm Innovation and Cluster Governance in the Canadian Automotive Parts Industry
Bibliographic record
Abstract
Abstract Based on a case study of the Canadian Auto Workers (CAW) union in southern Ontario we argue for a critical reconstruction of both the labour geography and industrial cluster literature. The former stresses the active role of labour in the formation of economic landscapes, but has yet to explore labour's agency in production and how labour institutions shape technological change, firm innovation and industrial policy and strategy. Conversely, much of the industrial cluster and regional innovation systems literature is silent on the role of unions and industrial relations institutions in fostering innovation. We conclude with two main points. First, while some contend that positive union roles in innovation can only stem from partnerships with management and team working, we argue that innovation is more likely to emerge and worker interests are better protected when traditional collective bargaining structures and progressive employment legislation play a central role. Second, positive workplace and cluster level cooperation in the Canadian automotive parts industry are jeopardized by the broader and ongoing macro‐economic restructuring of OEM global production networks due to over‐capacity and intense cost‐cutting pressures reverberating down the supply chain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".