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Record W4367556211 · doi:10.34069/ai/2023.62.02.28

Public official as a victim of criminal assault: comparative approach

2023· article· en· W4367556211 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRevista Amazonia Investiga · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Studies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsLaw enforcementCriminal liabilityEnforcementPolitical scienceCriminal lawLiabilityLawComparative lawComparative researchBusinessCriminologySociology

Abstract

fetched live from OpenAlex

This research paper aims to analyze criminal liability for assaulting law enforcement agents in different jurisdictions. A comparative approach is used to examine relevant criminal law provisions of several countries, including the United States, England, Germany, Canada, and Ukraine. The methodology combines statistical methods and comparative research to provide a detailed and comprehensive analysis. The most important findings indicate that some countries protect both public officials and law enforcement agents from illegal attacks, while others have a special liability regime for assaulting or threatening police officers only. In particular, it is argued that the Ukrainian approach is more balanced compared to other jurisdictions. Overall, the document provides a complete and detailed insight into criminal liability for assaulting law enforcement agents in various parts of the world.

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.

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)0.001
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: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.182
GPT teacher head0.357
Teacher spread0.174 · 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