Bibliographic record
Abstract
From Chinese factories making cheap toys for export, to sweatshops in Bangladesh where name-brand garments are sewn—studies on the impact of globalization on workers have tended to focus on the worst jobs and the worst conditions. But in When Good Jobs Go Bad , Jeffrey Rothstein looks at the impact of globalization on a major industry—the North American auto industry—to reveal that globalization has had a deleterious effect on even the most valued of blue-collar jobs. Rothstein argues that the consolidation of the Mexican and U.S.-Canadian auto industries, the expanding number of foreign automakers in North America, and the spread of lean production have all undermined organized labor and harmed workers. Focusing on three General Motors plants assembling SUVs—an older plant in Janesville, Wisconsin; a newer and more viable plant in Arlington, Texas; and a “greenfield site” (a brand-new, state-of-the-art facility) in Silao, Mexico— When Good Jobs Go Bad shows how global competition has made nonstop, monotonous, standardized routines crucial for the survival of a plant, and it explains why workers and their local unions struggle to resist. For instance, in the United States, General Motors forced workers to accept intensified labor by threatening to close plants, which led local unions to adopt “keep the plant open” as their main goal. At its new factory in Silao, GM had hand-picked the union—one opposed to strikes and committed to labor-management cooperation—before it hired the first worker. Rothstein’s engaging comparative analysis, which incorporates the viewpoints of workers, union officials, and management, sheds new light on labor’s loss of bargaining power in recent decades, and highlights the negative impact of globalization on all jobs, both good and bad, from the sweatshop to the assembly line.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.114 | 0.040 |
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".