Testing a Novel Child Farm Safety Intervention for Anabaptist Audiences
Bibliographic record
Abstract
Farming within the Anabaptist (within this article defined as various religious groups that do not believe in infant baptism; Amish, Hutterites, Mennonites, and Brethren are included within the definition and usually practice selective use of technology) population creates a unique situation with regards to teaching farm safety and health to children. Children working at an age younger than typical farm families, using older equipment and with increased exposure to livestock, affect injuries among Anabaptist children and youth. Addressing social differences and using low-tech methods for demonstrations help when planning resources and programs.An existing Farm Safety 4 Just Kids (FS4JK) magnetic farm scene, hazard hunt program was adapted for Anabaptist youth to include more images related to the Anabaptist lifestyle. The adapted educational display was piloted in 5 locations (Iowa, Pennsylvania, Ohio (2), and Ontario). Fifty-four boys and 54 girls participated in the pilot programs. Youth survey results indicated that animal-related issues were primary in Anabaptist youths' learning from the lessons. Behaviors most likely mentioned by youth as unsafe actions involved jumping off moving vehicles, mowing with bare feet, and approaching animals unsafely. Youth were most likely to change behavior when working with animals. Instructors indicated that the magnetic display medium was well received by the Anabaptist audience. Pilot testing led to adaptations to content and pictorial images.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".