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Record W2061241455 · doi:10.1002/iir.139

New legislative measures in South Africa aimed at combating over‐indebtedness—are the new proposals sufficient under the constitution and law in general?

2006· article· en· W2061241455 on OpenAlexvenueno aff
Stéfan Renke, Melanie Roestoff, Bernard Bekink

Bibliographic record

VenueInternational Insolvency Review · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationLegislatureConstitutionDebtLawBill of rightsState (computer science)Order (exchange)ConstitutionalismConsumer protectionConstitutional reviewEconomicsBusinessPolitical scienceLaw and economicsFinanceDemocracyPolitics

Abstract

fetched live from OpenAlex

Abstract The National Credit Bill codifies a number of fundamental rights of consumers in the credit market. It provides inter alia for a right to information to enable consumers to make informed choices and thereby contributes to one of the purposes of the proposed legislation, namely to provide mechanisms to combat over‐indebtedness. The main purpose of this research is to evaluate the proposed measures in the Bill aimed at combating over‐indebtedness and also to determine to which extent these measures comply with the general constitutional consumer protection demands. In order to achieve this, the relevant guidelines of the INSOL Consumer Debt Report and measures in other jurisdictions will also be considered. Since the South African Constitution does not directly obligate the state to enact specific credit laws and as the Bill seeks, in the spirit of the supreme law, to codify certain basic consumer rights, the new legislative initiatives are to be welcomed. It is, however, submitted that the Bill does not go far enough in achieving its particular aims and objectives and that more could be done to bring South African legislation in line with measures in other jurisdictions. Copyright © 2006 John Wiley & Sons, Ltd.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.047
GPT teacher head0.332
Teacher spread0.285 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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