Social reproduction and migrant labour: extending the view to Sicilian olive groves and tomato greenhouses
Bibliographic record
Abstract
Abstract Social reproduction offers a critical lens through which to analyse how labour law creates and constructs labour/ers. Socially reproductive work, traditionally ignored in waged labour markets, has been omitted from legal categories that protect workers. Yet these same legal categories that create and construct labour/ers are themselves socially reproduced. In Sicilian agricultural work, social reproduction happens in the extra care that is needed in labour carried out by migrantised workers, as well as the silence that is reproduced by markets that overlook the exploitation buttressing a local economy. The lens of social reproduction connects the work behind the scenes that depends on the complicity, whether wilful or ignorant, of consumers who do not ‘care’ that the labour producing Sicilian Denominazione di Origine Protetta (Protected Designation of Origin, DOP) and Indicazione Geografica Protetta (Protected Geographical Denomination, IGP) products is legally irregular. Contributing to discussions of labour law’s limits, this article addresses how labour exploitation is socially reproduced through the invisibilisation of labour involved in cultivating and harvesting Sicilian DOP olives and IGP tomatoes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".