Informality, Social Citizenship, and Wellbeing among Migrant Workers in Costa Rica in the Context of COVID-19
Bibliographic record
Abstract
Costa Rica is home to 557,000 migrants, whose disproportionate exposure to precarious, dangerous, and informal work has resulted in persistent inequities in health and wellbeing in the midst of the COVID-19 pandemic. We used a novel multimodal grounded approach synthesizing documentary film, experiential education, and academic research to explore socioecological wellbeing among Nicaraguan migrant workers in Costa Rica. Participants pointed to the COVID-19 pandemic as exacerbating the underlying conditions of vulnerability, such as precarity and informality, dangerous working conditions, social and systemic discrimination, and additional burdens faced by women. However, the narrative that emerged most consistently in shaping migrants' experience of marginalization were challenges in obtaining documentation-both in the form of legal residency and health insurance coverage. Our results demonstrate that, in spite of Costa Rica's acclaimed social welfare policies, migrant workers continue to face exclusion due to administrative, social, and financial barriers. These findings paint a rich picture of how multiple intersections of precarious, informal, and dangerous working conditions; social and systemic discrimination; gendered occupational challenges; and access to legal residency and health insurance coverage combine to prevent the full achievement of a shared minimum standard of social and economic security for migrant workers in Costa Rica.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".