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Record W2132937935 · doi:10.1177/1477370804044005

Organizational Pathologies in Police Intelligence Systems

2004· article· en· W2132937935 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Journal of Criminology · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsYork University
Fundersnot available
KeywordsLexiconStrategic intelligenceOrganised crimeVocabularyIntelligence cyclePolitical sciencePublic relationsCriminologyEmpirical researchCrime analysisEmpirical evidenceKnowledge managementSociologyComputer scienceMilitary intelligenceLawArtificial intelligence

Abstract

fetched live from OpenAlex

During the 1990s serious and organized crime moved to the top of the agenda for policing internationally. This shift took place during the same period that new information and communications technologies were being adopted across the policing sector in most European countries, a shift known in some places as the rise of ‘intelligence-led policing’. Discussion of intelligence-led policing against serious and organized crime has tended to focus on formal models of intelligence systems. The purpose of this paper is to contribute to the vocabulary of intelligence-led policing by providing the terms for describing the organizational problems that bedevil the organization of police information systems. It is based on original empirical research in the United Kingdom and a number of other countries and provides a lexicon of 11 organizational pathologies. The paper ends by arguing that strategic intelligence forecasts about future trends in organized and serious crime that emanate from the police sector are not as strategic or as comprehensive as they appear to be.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.088
GPT teacher head0.307
Teacher spread0.219 · 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