Occupational Health and Safety Standards of Foreign Seasonal Farm Workers: Evaluation of Personal Protection Measures, Policies and Practices
Bibliographic record
Abstract
Health and safety standards are paramount to all agricultural workers and more so to the foreign seasonal farm workers. European, North American and Oceanic agricultural sector heavily depends on the foreign workers migrating temporarily to carryout seasonal agricultural work that are not attractive to local citizens. The aim of this chapter is to critically analyze existing workplace health and safety measures, policies and practices of Foreign agricultural workers with a secondary focus on Canadian public health standards that applies to COVID-19 pandemic control and beyond. During the pandemic, many countries opened international labour migration as a measure of economic recovery. Recent news media reported two Caribbean workers in the Canadian Agricultural sector, had died of COVID-19 complications. The basis of this chapter is the research based evidence that the author carried out on occupational health and safety standards of the population of foreign seasonal farm workers using a multi-method data collection: a scoping review of existing standards, policies and practices and personal interviews with seasonal agricultural workers and their employers. This chapter provides a critical analysis of data from multiple sources and from multiple jurisdictions to uncover gaps and malpractices of existing occupational health and safety practice standards for illness and injury prevention of foreign seasonal farm workers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".