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Record W4361011086 · doi:10.26522/ssj.v17i1.4034

A Lost Opportunity? Collective Demands and Migrant Farmworkers in Costa Rica during the Pandemic

2023· article· en· W4361011086 on OpenAlexvenueno aff
Koen Voorend, Daniel Alvarado Abarca, Rónald Sáenz Leandro

Bibliographic record

VenueStudies in Social Justice · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)Collective actionNewspaperPandemicMigrant workersNIMBYPosition (finance)Political scienceCoronavirus disease 2019 (COVID-19)Economic growthDevelopment economicsBusinessEconomicsPolitics

Abstract

fetched live from OpenAlex

The COVID-19 pandemic induced an overexposure of migrant farmworkers’ poor working and living conditions in Costa Rica’s northern border area and underscored the country’s dependence on migrant labor. This created a unique opportunity to position pro-migrant concerns and demand actions from the state. In this article, we assess if and to what extent the actions of the Costa Rican state were influenced by migrant demands, or whether other priorities guided policy. Based on a novel database on protest and collective action (Protestas-IIS) that is fed with national and local newspaper articles, we analyze the demands made by migrants, the private sector and NIMBY movements, and state responses. Our findings suggest that the latter prioritized market concerns and antiimmigrant interests, thereby underscoring lessons from the literature that migrants are among the politically most disenfranchised in society. Their demands were only partially responded to by the state, and only concerning issues that aligned directly with public concerns, in this case related to health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.131
GPT teacher head0.346
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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