Launching an industry led coalition for safety and health of agricultural workers
Bibliographic record
Abstract
Agriculture is the most dangerous industry in the United States with an estimated annual 650 deaths, 90 000 injuries, and a death rate eight times greater than the all-industry average. Based on successes in Australia and Canada, we aimed to change this trend by having leaders within agricultural businesses and farm organisations become a unified voice for national agricultural safety initiatives. Beginning in 2006, we convened in-person meetings building momentum that launched the Agricultural Safety and Health Council of America (ASHCA) in late 2007. A compelling feature of this new organization was leadership dominated by people with authority to introduce safety measures within their respective agricultural networks. ASHCA leaders drafted a mission, governance structure, bylaws, and then identified strategic initiatives. ASHCAs Strategic Plan has priorities of: (a) promoting communications and partnerships; (b) promoting evidence-based best practices; (c) engaging with National Institute for Occupational Safety and Health (NIOSH) funded agricultural safety and health researchers and (d) guiding the research agenda of federal agencies, including NIOSH and US Department of Agriculture. Now 2 years later ASHCA has grown to encompass 30 organisations. In January 2010, ASHCA hosted a first of its kind national conference, Be Safe, Be Profitable: Protecting Workers in Agriculture, uniting agricultural leaders, safety practitioners, researchers and farm workers in determining the best safety practices for workers in agriculture. Proceedings from this groundbreaking conference will be published in a dedicated issue of the Journal of Agromedicine July 2010 and used as a reference for agricultural producers and safety professionals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".