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Record W4399040711 · doi:10.29173/mlj765

Bill 14: The Consumer Protection Amendment Act(Payday Loans)

2011· article· en· W4399040711 on OpenAlexaboutno aff
Nathan Irving

Bibliographic record

VenueManitoba Law Journal · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Contract Law
Canadian institutionsnot available
Fundersnot available
KeywordsAmendmentConsumer Protection ActBusinessConsumer protectionLawFinancePolitical scienceCommerce

Abstract

fetched live from OpenAlex

INTRODUCTIONver the past 20 years, stores offering -fast cash have cropped up all across Canada.Pictures of overjoyed, upwardly-mobile young adults holding fans of banknotes greet the customers, assuring them that despite their poor credit histories, they, too, will come away with cash in their pockets and smiles on their faces.If only this were the whole story.Until recently, the payday loan industry was largely unregulated in Canada.This billion-dollar industry, often accused of predatory lending practices, 1 was left alone to charge exceptionally high interest rates with little government interference.Payday lenders routinely charged interest rates exceeding 10 times the legal limit.While it is true that some lenders have been found liable in class action lawsuits and some lenders have been refused the courts' assistance in enforcing these illegal contracts, not a single payday lender has ever been criminally prosecuted for usury. 2 Bill 14, The Consumer Protection Amendment Act (Payday Loans), 3 is the latest effort by the Government of Manitoba to regulate the payday loan industry and, purportedly, protect consumers.Bill 14 was introduced in the House on 8 April 2009 and received Royal Assent on 11 June 2009.This paper provides an overview of the events giving rise to the Bill, outlines its key provisions, and describes its passage through the House.The paper concludes with an assessment of the Bill. 1

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.090
GPT teacher head0.277
Teacher spread0.187 · 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.

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

Citations1
Published2011
Admission routes1
Has abstractyes

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