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Record W4281657874 · doi:10.1177/13624806221105280

Transnational policing between national political regimes and human rights norms: The case of the Interpol Red Notice system

2022· article· en· W4281657874 on OpenAlexaff
Serdar San

Bibliographic record

VenueTheoretical Criminology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAuthoritarianismDemocracyHuman rightsIdeologySolidarityPolitical sciencePoliticsNoticeLawSociologyState (computer science)TurkishPolitical economy

Abstract

fetched live from OpenAlex

Current transnational policing mechanisms such as Interpol appear to reproduce authoritarianism-like actions in democratic contexts by helping to undermine the rights and freedoms of individuals targeted by non-democratic regimes. Through an in depth examination of the cases of Turkish and Russian police, this article seeks to explain the possible motives of the law enforcement institutions of democratic states in executing the questionable Interpol Red Notice requests by authoritarian regimes based on the existing theoretical debates in the literature on international policing. It explores three factors that foster policing cooperation between democratic and authoritarian states: 1) an aspired depoliticization of international policing that facilitates cooperation among states with different national and ideological outlooks; 2) an occupational culture that encourages professional support and solidarity among policing agents that transcends national rivalries; and 3) state cooperation against threats posed by the planning and conduct of international crime.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.019
Scholarly communication0.0060.004
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.345
Teacher spread0.297 · 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 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

Citations12
Published2022
Admission routes1
Has abstractyes

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