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Record W3020459235 · doi:10.1177/1477370820913465

Playing Pac-Man in Portville: Policing the dilution and fragmentation of drug importations through major seaports

2020· article· en· W3020459235 on OpenAlexaboutno aff
Anna Sergi

Bibliographic record

VenueEuropean Journal of Criminology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
FundersBritish Academy
KeywordsDrug traffickingLaw enforcementBorder SecurityBusinessConfidentialityComputer securityEnforcementFragmentation (computing)Political scienceLawPublic administrationCriminologySociologyComputer science

Abstract

fetched live from OpenAlex

This article presents findings from a qualitative research project into organized crime, policing and security across five major seaports (‘Portvilles’): Genoa (Italy); Melbourne (Australia); Montreal (Canada); New York (USA); and Liverpool (UK). Through content analysis of confidential judicial files, the article will construct the offenders’ scenarios and options for importing drugs in Portville. Through also interviews with law enforcement agencies, police forces and security staff in these seaports, the article presents the policing and security struggles to disrupt importations. The main finding is that importation roles and security techniques change constantly and quickly, as in a game of Pac-Man. Security and policing in seaports lead to the dilution and fragmentation of drug importation, and only distribution tends to remain organized in Portville. In this chaotic environment, it is the rules of trade that affect the success of drug importations the most, rather than the failures of effective security and policing.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.010
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.070
GPT teacher head0.311
Teacher spread0.240 · 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 designQualitative
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

Citations24
Published2020
Admission routes1
Has abstractyes

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