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Record W2111984693 · doi:10.7202/043495ar

Quebec's Comprehensive Auto No-Fault Scheme and the Failure of Any of the United States to Follow

2005· article· en· W2111984693 on OpenAlexvenueaboutno aff
Stephen D. Sugarman

Bibliographic record

VenueLes Cahiers de droit · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyGovernment (linguistics)IndividualismState (computer science)TortPoliticsPosition (finance)Power (physics)Political scienceLaw and economicsLawEconomicsLiabilityFinance

Abstract

fetched live from OpenAlex

Although Quebec's no-fault auto insurance scheme has served for 20 years as an exemplary model to follow, so far not one of the United States has adopted anything even close to it. This article examines the reasons for that failure, both in California and throughout the country. Emphasis is given to several factors that stand in the way of U.S. reform and that may distinguish states in the U.S. from Canadian provinces generally and Quebec in particular: 1. State politics — the power of the lawyers who represent victims, the position of the insurers, and the structure of state government. 2. Public perceptions — negative attitudes towards government, the insurance industry, and the prospects of saving money on auto insurance premiums. 3. Traditions—the ideological strength of individualism and ideological weakness of collective responsibility. 4. Tradeoffs — doing away with the tort system means giving up more in the U.S. than elsewhere. 5. Policy concerns — fears about safety, costs, and the « slippery slope ». Finally, the possibility that one or more U.S. states might in the future evolve towards the Quebec solution is explored.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.262
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2005
Admission routes2
Has abstractyes

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