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Record W3139280810

Consumer Credit in Canada: A Regulatory Patchwork

2020· article· en· W3139280810 on OpenAlexaboutno aff
Micheline Gleixner

Bibliographic record

VenueeYLS (Yale Law School) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Contract Law
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessConsumer protectionCommerce
DOInot available

Abstract

fetched live from OpenAlex

Vu l’accès illimité au crédit à la consommation et son utilisation accrue qui en résulte au Canada, et compte tenu de l’abandon progressif par le gouvernement fédéral de toute réglementation à cet égard depuis le début de la Confédération, les provinces et territoires ont progressivement adopté des lois provinciales et territoriales de protection des consommateurs visant à réglementer le secteur du crédit à la consommation et à protéger les consommateurs vulnérables. Un examen des cadres législatifs provinciaux et territoriaux actuels régissant le crédit à la consommation révèle des divergences et des limites importantes. Compte tenu de l’expansion du secteur du crédit à la consommation et de la vulnérabilité inhérente des consommateurs, le présent article confirme la nécessité et l’urgence de renforcer la protection financière des consommateurs et propose des pistes de réforme possibles. Il est recommandé que le Parlement réaffirme sa compétence fédérale prépondérante sur la question des intérêts afin de mettre en œuvre un cadre national complet en matière de crédit à la consommation et de promouvoir un secteur du crédit durable et responsable tout en veillant à ce que les consommateurs canadiens soient non seulement mieux protégés contre les pratiques de prêt abusives et usuraires, mais aussi mieux équipés pour accroître leur santé et leur bien-être financiers.

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)0.010
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.196
Threshold uncertainty score0.932

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0110.007
Scholarly communication0.0110.002
Open science0.0040.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.000

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 head0.022
GPT teacher head0.250
Teacher spread0.228 · 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 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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