Canada Should Move Toward Adopting Harmonized Evidence-Based OELs to Consistently and Adequately Protect Workers
Bibliographic record
Abstract
Due to the way occupational exposure limits (OELs) are set in Canada, workers across the country are not equally and adequately protected from harmful workplace exposures. This disparity is illustrated in the case of exposure to diesel engine exhaust (DEE). Based on the findings of a recent pan-Canadian and international scan of OELs for DEE, we recommend that Canada overcome these current disparities by moving towards harmonized, evidence-based OELs. To achieve this, Canada should adopt a centralized framework for setting OELs that considers the most recent scientific evidence as well as feasibility of implementation in the Canadian context. We assert that harmonizing OELs across Canada would allow for expertise and resources to be consolidated and is a crucial step to ensuring that all workers are consistently protected from harmful workplace exposures.
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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 | 0.118 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.013 | 0.013 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 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".