Bibliographic record
Abstract
From the Jaws of Victory: The Triumph and Tragedy of Cesar Chavez and the Farm Worker Movement is the most comprehensive history ever written on the meteoric rise and precipitous decline of the United Farm Workers, the most successful farm labor union in United States history. Based on little-known sources and one-of-a-kind oral histories with many veterans of the farm worker movement, this book revises much of what we know about the UFW. Matt Garcia’s gripping account of the expansion of the union’s grape boycott reveals how the boycott, which UFW leader Cesar Chavez initially resisted, became the defining feature of the movement and drove the growers to sign labor contracts in 1970. Garcia vividly relates how, as the union expanded and the boycott spread across the United States, Canada, and Europe, Chavez found it more difficult to organize workers and fend off rival unions. Ultimately, the union was a victim of its own success and Chavez’s growing instability. From the Jaws of Victory delves deeply into Chavez’s attitudes and beliefs, and how they changed over time. Garcia also presents in-depth studies of other leaders in the UFW, including Gilbert Padilla, Marshall Ganz, Dolores Huerta, and Jerry Cohen. He introduces figures such as the co-coordinator of the boycott, Jerry Brown; the undisputed leader of the international boycott, Elaine Elinson; and Harry Kubo, the Japanese American farmer who led a successful campaign against the UFW in the mid-1970s.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".