Workers’ Compensation Experience-Rating Rules and the Danger To Workers’ Safety in the Temporary Work Agency Sector
Bibliographic record
Abstract
By putting a price on workplace health and linking this to costs incurred in individual businesses, experience-rating rules encourage employer ‘gaming’ — cost-reduction attempts that do not necessarily increase workplace safety. We argue that experience-rating rules, together with the rise of non-standard employment arrangements, have fostered the growth of the temporary work agency sector to which employers can outsource workplace injury risk.This study explored how temporary work agencies in Ontario, Canada carry out workplace injury prevention and return to work. We aimed to understand why these agencies would shoulder experience-rating costs when they cannot control the work environment. Focus groups and in-depth interviews were held with 64 participants between 2009 and 2011. Participants included low-wage agency workers, temporary work agencies, client employers and key informants. Legal and documentary data were also analysed.Our findings show how experience-rating rules create a market for outsourcing risky jobs to temporary work agencies, which cannot properly manage injury prevention and return to work. We detail how agencies are positioned to absorb experience-rating costs for their clients and avoid financial risk through cost transfer, premium rate groups, legal positioning, influencing accident reporting practices, and shutting down and re-opening the business.Our findings also show how experience-rating arrangements can distort the responsibility these agencies have for work and health. In Ontario, these facilitate employer ‘gaming’, largely within the rules. We propose that workplace health would be less of a tradeable commodity, and workers’ safety and return to work a more significant priority for employers, if experience rating were applied to the client employer who controls the conditions of work.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".