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Record W2027725691 · doi:10.7202/042404ar

Les critères de répartition des normes entre la loi et le règlement

2005· article· en· W2027725691 on OpenAlexvenueno aff
Jean Alarie, Guy Boisvert

Bibliographic record

VenueLes Cahiers de droit · 2005
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsStatuteLegislatureNormativePolitical scienceGeneralityLawPoliticsManagementEconomics

Abstract

fetched live from OpenAlex

Various groups and institutions are concerned about the proliferation of regulations. There is fear of an overflow into the regulatory field of standards that belong to the legislative domain. It therefore seems appropriate to draw up criteria for assigning standards to the respective areas of statutes and regulations. The logic for apportioning standards might be based on the principle that the placing of a standard in the hierarchy of unilateral normative instruments is directly related to the scope of its object and inversely related to its degree of intervention in the activity subjected to it. Before deciding to put a standard into a statute or to provide that it be enacted later under regulation-making authority, it is advisable to assess its degree of generality in relation to the concepts that surround it in the legislative draft, and also to measure its degree of materiality, i.e. the extent to which it embodies Government intervention in the activity that it is proposed to regulate. From a practical standpoint, however, the impact of the political environment on legislative drafting must not be ignored. Allowing for the influence of policy and politics on the design of the statute-regulations complex, three contiguous but distinct normative areas may be identified: standards that must belong to the domain of statutes ; standards that may be apportioned to either the domain of statutes or that of regulations ; and standards that would normally belong to the domain of regulations. The model suggested is not absolute and is liable to be modified by space and time considerations ; however, reference to it might help to rationalize the delegation of regulation-making authority.

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)0.045
metaresearch head score (Gemma)0.099
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: Other · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.099
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0070.004
Science and technology studies0.0070.025
Scholarly communication0.0180.015
Open science0.0040.004
Research integrity0.0040.015
Insufficient payload (model declined to judge)0.0060.004

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 head0.057
GPT teacher head0.429
Teacher spread0.372 · 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
GenreOther

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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