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Record W1534718096 · doi:10.1177/203228441400500307

Do We Really Need Criminal Sanctions for the Enforcement of EU Law?

2014· article· en· W1534718096 on OpenAlexaboutno aff
Jacob Öberg

Bibliographic record

VenueNew Journal of European Criminal Law · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatorSanctionsPolitical scienceCriminal lawLawEnforcementCompetence (human resources)LegislationEuropean unionLaw enforcementCriminal procedureLaw and economicsBusinessEconomics

Abstract

fetched live from OpenAlex

This article examines how the ‘essentiality’ requirement can limit the exercise of the EU's criminal law competence under Article 83(2) TFEU. Building on criminological research, and contextual and principled considerations, it argues for an evidence-based approach to the ‘essentiality’ criterion. It sustains that the Union legislator must show by empirical proof that criminal laws are more ‘effective’ than non-criminal sanctions in the implementation of a specific EU policy. The article proposes that judicial enforcement is a key mechanism for implementing the ‘essentiality’ criterion. On the basis of the Court's rulings in Kadi II and Tetra Laval a strict procedural test for review of criminal law legislation is suggested. It entails that the EU legislator must show that the justification for exercising the EU's criminal law competence is substantiated by relevant evidence. Because criminal penalties entail severe consequences for individuals and potentially breach their fundamental freedoms such a stringent test is justified.

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)0.027
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 score0.027
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.099
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.019
Scholarly communication0.0160.024
Open science0.0020.008
Research integrity0.0150.013
Insufficient payload (model declined to judge)0.0080.001

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 head0.073
GPT teacher head0.329
Teacher spread0.256 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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