Bibliographic record
Abstract
Abstract On 15 September 1970, over 400,000 workers struck General Motors (GM), the biggest corporation in the world. It was a massive walkout, lasting sixty-seven days and affecting 145 GM plants in the US and Canada. GM lost more than $1 billion in profits, and the impact on the US economy was considerable. Despite the strike's size, it has been understudied. Fifty years later, this article provides a re-assessment of this landmark dispute, the first to use detailed archival records of the strike. Refuting claims that the strike lacked drama, I argue that this was a multifaceted – and compelling – story. Primary sources show that workers and union leaders were heavily invested in the battle, which reflected deep-seated local, national, and global issues. The United Automobile Workers (UAW) mobilized significant levels of national and international support, and won a range of concessions, including substantial wage and benefit increases and the ability for workers to retire after thirty years’ service. The strike was deeply infused by local issues and should not just be viewed through the lens of the national GM–UAW relationship. In a broader context, the strike is also important because it occurred at a time of rising global labour militancy, which scholars are increasingly recognizing. Its story contributes to a growing body of literature on the 1970s, a decade that witnessed important activism in many areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".