Have the Politics of Rate Regulation Produced a Better No-Fault Regime for Ontario ?
Bibliographic record
Abstract
Ontario has changed its no-fault legislation substantially three times in the past decade. These changes have reflected the interest group lobbying of the insurance industry and the practising bar. However, the main and explicit motivation, especially for the latest revision, has been the government's desire to regulate rates. With the Automobile Insurance Rate Stability Act the government appears to have struck a very successful compromise. The lawyers have been allowed an increased, albeit limited, right to sue in tort. The insurers have achieved more certainty, with stricter time and monetary limits on benefits for non-catastrophic injury. Rates have been reduced in part through lower benefit levels, but primarily by throwing the cost of automobile accidents on to other collateral sources. There is, therefore, some subsidization of driving inherent in the legislation. There are also compensation gaps, especially in long term health care, that affect mainly the most vulnerable members of society. Both these shortcomings could and should be easily corrected. So far, it would appear that the politics of rate regulation have generated an improved no-fault automobile accident compensation scheme for Ontario.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".