HEALTH AND HEALTHCARE ACCESS FOR UNDOCUMENTED MIGRANT AGRICULTURAL WORKERS IN GREECE
Bibliographic record
Abstract
There are an estimated 200,000 Bangladeshi, Pakistani, and Indian migrants in Greece, most of whom are undocumented men. Undocumented migrant workers are estimated to make up 90 percent of agricultural labour in Greece. The nature of agricultural work significantly increases risk of injury and illness for workers through demanding physical labour, occupational stress, and exposure to pesticides. Under Greek law, undocumented migrants have access to free public healthcare only in emergency situations but must pay out-of-pocket otherwise. This project, driven by the political economy of migration and discourses of health, race, and citizenship looks at how health outcomes and healthcare access for undocumented migrant workers in Greece. It asks how social, political and economic structures, including citizenship status, policies of migration governance, healthcare costs, and racism, impact health outcomes and encounters with the healthcare system for migrant workers. Examining the case study of South Asian migrant men working in the fields around the two agricultural towns of Manolada and Megara, this thesis will draw on an intersectional theoretical framework that combines concepts from critical political economy, migration studies, and health anthropology, to demonstrate how migrant workers experience worse health outcomes as a result of the structural vulnerabilities engendered within racial capitalism and their “illegal” citizenship status. These structural vulnerabilities produce and organize the everyday suffering of workers on a through the enacting of labour demands, the enforcement of such demands, and financial pressure, and have been central in upholding the exploitative conditions in which migrants live and work. The disposability of migrant workers is compounded by “illegal” citizenship status, as undocumented workers are denied the rights and protections of the state and are under constant threat of deportation and detention. Both institutional and societal discourses of migrant “illegality” and anti-migrant racism work to reproduce the exploitative labour arrangement and serve to justify the state of disposability and precarity in which migrant workers live.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".