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Record W143540819 · doi:10.5539/ilr.v5n1p16

The Appropriate Level of Enforcement in Multilevel Regulation Mapping Issues in Avoidance of Regulatory Overstretch

2016· article· en· W143540819 on OpenAlexvenueno aff
Michiel A. Heldeweg, Ramses A. Wessel

Bibliographic record

VenueInternational Law Research · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnforcementSubsidiarityLaw and economicsNorm (philosophy)Matching (statistics)Coercion (linguistics)Deterrence theoryBusinessRegulatory statePolitical scienceLawEconomicsEuropean unionInternational trade

Abstract

fetched live from OpenAlex

In modern day ‘multilevel’ regulation, legal enforcement of transnational standards usually takes place at another level than where the norm was created in the first place. The quest for smart regulation begs the question which level is most appropriate to proper legal enactment and enforcement. Not only as a matter of location (e.g. inter- or supranational, or domestic), but also concerning the level of strength (i.e. intensity of prescription and/or coercion). How can ‘regulatory overstretch’ be avoided – given smart regulatory principles of subsidiarity and responsiveness? A general description is provided of regulatory enforcement, from a regulatory and an enforcement perspective, followed by a discussion of appropriateness of enforcement from the dimensions of location-level and of strength-level. Finally, a simple model is presented for a ‘remediableness analysis’ of appropriate matching of strength and location level scenarios of enforcement – as guidance for enactment and enforcement of transnational standards without regulatory overstretch.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.366

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.210
GPT teacher head0.376
Teacher spread0.166 · 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 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

Citations3
Published2016
Admission routes1
Has abstractyes

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