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Record W2088774305 · doi:10.7202/043502ar

L'indemnisation des victimes en fonction des pertes non économiques résultant de blessures ou de décès : régime d'État ou de droit commun?

2005· article· fr· W2088774305 on OpenAlexvenueno aff
René Letarte

Bibliographic record

VenueLes Cahiers de droit · 2005
Typearticle
Languagefr
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesAutomobile insurancePhilosophyBusiness

Abstract

fetched live from OpenAlex

Deux grands régimes juridiques disposent des pertes non pécuniaires : le régime de droit commun et la Loi sur l'assurance automobile. La présente étude vise à brosser un tableau des deux régimes pour en faire ressortir les différences fondamentales, les avantages ainsi que les inconvénients. Le droit commun emprunte la voie traditionnelle de la responsabilité civile : « toute personne est responsable du dommage causé par sa faute ». Le droit civil recherche une indemnisation intégrale de la victime, mais nécessite, la plupart du temps, l'intervention du système judiciaire. Établie selon une philosophie complètement différente, la Loi sur l'assurance automobile abandonne la notion de responsabilité, indemnise la victime sans égard à la faute et ne recherche pas la compensation intégrale. Les dommages non pécuniaires sont établis à partir de grilles d'évaluation strictes beaucoup moins souples que les méthodes de calcul du quantum issues du droit civil.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.029
GPT teacher head0.340
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2005
Admission routes1
Has abstractyes

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