An Imperfect Solution: Why the Current H-2A Visa Program Cannot Make up for Deported Farm Laborers
Bibliographic record
Abstract
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 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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".