Who to Inspect? Using Employee\nComplaint Data to Inform Workplace\nInspections in Ontario
Bibliographic record
Abstract
En Ontario, comme dans plusieurs autres provinces, l’application des normes d’emploi comporte la conduite d’enquêtes à la suite de plaintes formulées par des employés et, dans une moindre mesure, la réalisation proactive d’inspections des lieux de travail. Des analyses des données administratives du ministère du Travail de l’Ontario révèlent que les données relatives aux plaintes sont très peu utilisées pour étayer les inspections des lieux de travail. Or, le respect strict des procédures du ministère en matière d’inspection des lieux de travail ne débouche pas sur l’examen de certaines des plaintes factuellement les plus courantes. Les auteures préconisent donc une mise en application plus stratégique grâce à l’utilisation accrue des données relatives aux plaintes pour orienter les inspections des lieux de travail déclenchées par les plaintes et au recours plus fréquent aux pénalités dans le cadre de ces inspections. Abstract: In Ontario, as in many other jurisdictions, employment standards enforcement includes reactively investigating employee complaints and, to a lesser extent, proactively inspecting workplaces. Analyses of administrative data from Ontario’s Ministry of Labour (MOL) show that the use of complaint data to inform workplace inspections is quite limited. Strict adherence to the MOL’s procedures for workplace inspections is not conducive to the investigation of some of the most common empirical complaints. Accordingly, we argue for more strategic enforcement by making greater use of complaint data to guide workplace inspections triggered by complaints and for the increased use of penalties in these inspections.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".