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Record W3141356556 · doi:10.3390/ijerph18073675

Precarious Essential Work, Immigrant Dairy Farmworkers, and Occupational Health Experiences in Vermont

2021· article· en· W3141356556 on OpenAlexaboutno aff
Bindu Panikkar, Mary-Kate Barrett

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational safety and healthEnvironmental healthHarmQuarter (Canadian coin)ImmigrationWork (physics)WelfareMedicineSocioeconomicsBusinessPsychologyGeographyPolitical scienceSociologySocial psychology

Abstract

fetched live from OpenAlex

Migrant dairy workers in Vermont face a wide range of occupational and health hazards at work. This research examines the environmental risks, occupational health hazards, and health outcomes experienced by migrant dairy farm workers in Vermont. This research draws on a triangulation of sources including analysis of data-surveys and interviews with migrant dairy farmworkers gathered by the organization Migrant Justice since 2015 as well as relevant key informant interviews with community organizations across the state to characterize the occupational health experiences of migrant dairy workers in Vermont. Our results show that Vermont migrant dairy farmworkers received poor health and safety training and lacked sufficient protective gear. Over three quarters of the respondents reported experiencing harm from chemical and biological risks. Close to half the survey respondents reported headaches, itchy eyes and cough; a quarter reported breathing difficulties; three fourths reported being hurt by animal-related risks. These exposures and existing health concerns are avoidable. Migrant workers require better social representation and advocates to negotiate better work-related protection and training, access to health services, and social welfare to ensure their health and safety.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.049
GPT teacher head0.329
Teacher spread0.280 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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