Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it