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Record W7112621289

An Imperfect Solution: Why the Current H-2A Visa Program Cannot Make up for Deported Farm Laborers

2025· article· W7112621289 on OpenAlexaboutno aff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsnot available
Fundersnot available
KeywordsDeportationQuarter (Canadian coin)AgriculturePaymentImperfectVariable (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

With deportation efforts underway, many criticisms have been raised regarding how President Trump’s executive orders might affect the agriculture industry. It is no secret that a large portion of farm workers in the United States are unauthorized. In 2022, the United States Department of Agriculture put the number of unauthorized crop farmworkers at 42% of the total workforce. The Center for Migration Studies estimates that the number of unauthorized agriculture workers is around 283,000; however, this number is highly variable and is likely much larger due to workers not wanting to self-report their status as unauthorized. The significant portion of these workers come from Mexico and about half are working in California. The wisdom of President Trump’s deportation policy is debatable, but what is certain is that over a quarter of a million people in the United States should not be left in a legal limbo, simultaneously performing critical labor and risking deportation at any moment.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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.

Study designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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