Bibliographic record
Abstract
Abstract Although the term has slightly different meanings in different contexts, the most precise and inclusive definition for a migrant farm worker is “itinerant harvester or planter.” This reflects the reality that virtually all farm laborers in the United States are involved in the labor‐intensive processes found at the beginning or at the end of the growing cycle. As one commentator noted, the term “migrant farm worker” is the commonly accepted terminology because “itinerant” lacks phonetic grace and “seasonal farm worker” erroneously assumes that all seasonal work involves long‐distance travel (Moore 1965: xi). A migrant farm worker is defined by US law as someone “whose employment requires travel that preclude[s] the farm worker from returning to his or her domicile within the same day” (Migrant and Seasonal Agricultural Workers Protection Act, codified as 29 USC 1802). Notwithstanding legal definitions, the term is best understood in conjunction with the unique circumstances that led to the current agricultural economy, which manages to feed the nation by employing less than 2 percent of the workforce (Rothenberg 1998: 62).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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".