Changing the Child Labor Laws for Agriculture: Impact on Injury
Bibliographic record
Abstract
OBJECTIVE: The child labor laws are intended to protect young workers from the most dangerous jobs. However, children who work on their parents' farms are exempt from these laws. We evaluated the potential for preventing the occurrence of farm injuries among children by changing the US Federal Child Labor Laws, Hazardous Occupations Orders for Agriculture. METHODS: A retrospective case series of 1193 farm injuries among children from the United States and Canada was assembled. The Hazardous Occupations Orders were systematically applied to each case. Injury preventability was estimated. RESULTS: A total of 286 (24%) cases of injury involved immediate family members engaged in farm work. Among these children, 33% of those aged younger than 16 years and 36% of those aged 16 or 17 years were performing work prohibited under the Hazardous Occupations Orders. CONCLUSIONS: Removing the family farm exemption from the Hazardous Occupations Orders and raising the age restriction for performing hazardous agricultural work from 16 to 18 years would be efficacious in preventing the most serious injuries experienced by young family farm workers. Potential reductions in injury would meet Healthy People 2010 goals for reducing traumatic injury in the agricultural sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".