Farm safety: A prerequisite for sustainable food production in Newfoundland and Labrador
Bibliographic record
Abstract
A sustainable approach to food production must address both environmental sustainability and the wellbeing of food producers. Farming is one of the most dangerous occupations globally with high rates of injury, fatality, and occupational disease. However, occupational hazards and the practices that lead to unsafe working environments are often overlooked in sustainable food system research. Poor management of occupational health and safety (OHS) can potentially threaten the survival of individual agricultural operations through injury and illness of the operator, family members, and employees. Gaps in agricultural safety knowledge, prevention, and compensation have been unevenly addressed in Canada. This paper presents findings from the first study of agricultural OHS in Newfoundland and Labrador (NL). Findings from a 2015-2016 survey of 31 food-producing operators representing 34 large and small operations in three NL regions show: 1) that hazards present within these operations are similar to those found in other contexts; 2) accidents are relatively common and most are not reported to workers’ compensation; 3) some participating operators were unsure whether their farms are subject to the regulations in the NL OHS Act; and, 4) there are gaps in workers’ compensation coverage. Some reliance on local and international volunteers and limited safety training point to other potential vulnerabilities. Study findings highlight the need to incorporate a focused strategy for injury prevention and compensation into efforts to develop a stronger and more sustainable food system in NL. We outline an agenda for future action relevant for NL and other places facing similar gaps and challenges.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".