Bibliographic record
Abstract
Background: Each year, the Canadian government assigns inspectors to visit firms and ensure compliance to the occupational health and safety provisions of the Canada Labour Code. This paper presents evidence supporting the hypothesis that assignments reduce the number of disabling injuries. It also presents a mathematical technique improving the performance of how firms are chosen for inspection. Methods: By combining two administrative databases covering worksites under federal jurisdiction, the impact of assignments on injuries is estimated using instrumental variables. The instruments are the exogenous part of the government’s assignment rule for inspectors. Results: The impact of one assignment on injuries is estimated to be -0.33 in the year following the assignment. Furthermore, by introducing a measure of diminishing marginal returns of assignments by sector, a more efficient assignment rule to help further minimize the number of injuries resulting from non-compliance with the Labour Code is suggested. This improved rule includes a reallocation of assignments across industries compared to the current allocation. In particular, assignments are transferred from the sectors of Air Transport and Road Transport to the sectors of Banking, Postal Contractors and Crown Corporations. Conclusions: A counterfactual experiment with the new allocation of assignments suggests an 18% decrease in the number of injuries for the same yearly average number of assignments. Based on available estimates of the cost of injuries, the suggested change in the targeting tool would lead to annual savings of 72 million dollars in workers’ compensation claims.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".