Guidelines for Children's Work in Agriculture: Implications for the Future
Bibliographic record
Abstract
The North American Guidelines for Children's Agricultural Tasks (NAGCAT) were developed to assist parents in assigning developmentally appropriate and safe farm work to their children aged 7-16 years. Since their release in 1999, a growing body of evidence has accumulated regarding the content and application of these guidelines to populations of working children on farms. The purpose of this paper is to review the scientific and programmatic evidence about the content, efficacy, application, and uptake of NAGCAT and propose key recommendations for the future. The methods for this review included a synthesis of the peer-reviewed literature and programmatic evidence gathered from safety professionals. From the review, it is clear that the NAGCAT tractor guidelines and the manual material handling guidelines need to be updated based upon the latest empirical evidence. While NAGCAT do have the potential to prevent serious injuries to working children in the correct age range (7-16 years), the highest incidence of farm related injuries and fatalities occur to children aged 1-6 years and NAGCAT are unlikely to have any direct effect on this leading injury problem. It is also clear that NAGCAT, as a voluntary educational strategy, is not sufficient by itself to protect children working on farms. Uptake of NAGCAT has been sporadic, despite being geographically widespread and has depended, almost solely, on a few interested and committed professionals. Key recommendations for the future are provided based upon this review.
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 | 0.038 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".