“We Can't Give Up. It's Too Important.” Health and Safety Stories from Canadian and U.S. Schools
Bibliographic record
Abstract
Schools are supposed to be places where children learn and thrive; not where they, teachers, and other staff get sick. The hazards are many but recognition of those hazards is hard to come by in schools in Canada and the United States. The result can be an uphill fight for school-based organizations and unions. Representatives of four such groups, two each from Canada and the United States, discuss the hazards and their effects. They also have many-often unrecognized-successes and related lessons to share. These include taking comprehensive approaches, looking for broad sweeps and entrees, using building sciences and strategies of solid information, acting with respect and with persistence, including students and parents, going for green cleaners, and using participatory methods. The representatives build on these to discuss what else needs to be done. The ideas are underpinned by the creativity, dedication, and persistence evident in their work to date.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.020 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".