Seasonal workers in Mediterranean agriculture: The social costs of eating fresh by Jörg Gertel and Sarah Ruth Sippel (Eds.)
Bibliographic record
Abstract
One of the most common justifications for maintaining low-paid, precarious conditions for farm workers is that while farmers are being squeezed by globalized competition, economic turmoil and increasingly unpredictable weather patterns, labour remains one of the few costs they can control. This lends a Thatcherian logic of “no alternative” to the expanding complexes of seasonal labour migration, which mobilize workers from economically marginalized regions of the world to orchards, fields, and greenhouses in wealthier nations. Seasonal Workers in Mediterranean Agriculture compellingly portrays how migrants bear the harshest costs of procuring year-round fresh fruits and vegetables for a privileged few. While giving voice to the social inequality that fuels the dominant agri-food system, the authors aim to show how the stretching of growing seasons and national borders has made room for new forms of insecurity and profitability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".