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Record W2774775880 · doi:10.1093/police/pax091

Do We Need to Know Each Other? Bridging the Gap Between the University and the Professional Field

2017· article· en· W2774775880 on OpenAlexaff
Simon Baechler

Bibliographic record

VenuePolicing A Journal of Policy and Practice · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsBridging (networking)SociologyPublic relationsPerspective (graphical)Field (mathematics)Criminal justiceEngineering ethicsProfessional developmentPosition (finance)Political sciencePedagogyCriminologyEngineeringComputer scienceBusinessComputer security

Abstract

fetched live from OpenAlex

The university and the policing professional field are commonly viewed as separate worlds that have trouble working together, each with their own objectives, values, methods, and processes. It is argued that they need to know each other better and to collaborate for the best of education, professional practice, and research. To achieve this, crime, security, and policing, rather than technology, have to be recognized as a common object of interest. In this perspective, the article describes the specific position and culture of the School of Criminal Justice of the University of Lausanne, Switzerland, in terms of proximity with policing organizations. It details the means developed over a century to build strong and fruitful relationships, such as shared PhD-professional positions, continuous education and training courses, collaborative student projects, or community-building initiatives. The advantages of such a close relationship, potential risks and limitations attached to it are discussed. The article advocates putting science and problem-solving at the crossroads of both worlds, and calls for an open-minded and optimistic approach of relationships between policing organizations and the university.

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 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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.099
GPT teacher head0.454
Teacher spread0.355 · 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 teacher head, not a consensus.

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

Citations15
Published2017
Admission routes1
Has abstractyes

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