Organization of work in the agricultural, forestry, and fishing sector in the US southeast: Implications for immigrant workers' occupational safety and health
Bibliographic record
Abstract
BACKGROUND: There is widespread agreement that work organization is an important element of occupational safety and health, but the health effects of many aspects of work organization are likely to vary considerably across different sectors of work and geographies. METHODS: We examined existing employment policies and work organization-related research relevant specifically to immigrant workers in the Agriculture, Forestry, and Fishing (AgFF) Sector of the US workforce focusing, when possible, on the southeastern US. RESULTS: A number of specific aspects of work organization within AgFF subsectors have been described, but most of this literature exists outside the purview of occupational health. There are few studies that directly examine how attributes of work organization relevant to the AgFF Sector affect workers', much less immigrant workers', occupational health exposures and outcomes. CONCLUSIONS: In contrast to the broader literature, research linking occupational health outcomes to work organization in the AgFF Sector is limited and weak. A systematic program of research and intervention is needed to develop strategies that eliminate or substantially mitigate the deleterious health effects of occupational exposures whose origins likely lie in the organization of AgFF work.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".