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Record W2283386173 · doi:10.3917/rfsen.533.0347

10.3917/rfsen.533.0347

2000· article· en· W2283386173 on OpenAlexvenueno aff

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyOfficerCriminologySocializationPoliticsPsychologyClass (philosophy)Political scienceSocial psychologySociologyLaw

Abstract

fetched live from OpenAlex

In the absence of surveys on how police officers vote, studying professional ideologies, which are an expression of a form of politicization of ordinary police officers, which is an integral part of their professional socialization, allows us to approach political orientations in the world of the police. Using a questionnaire survey of 5,221 police officers of all ranks, we identified ideological dissension on how the job of police officer is conceived. The analysis was based on a latent class analysis, which brings to light three opinion classes: repressive, median and preventive. These three profiles allow us to distinguish police officers by their responses to the questions of what the primary tasks of the police are, which groups should be monitored, who the police should choose as partners, and the causes of delinquency. Analysis of the social and professional attributes of police officers in each of the three opinion classes then reveals the preponderance of professional characteristics over socio-demographic variables in the three classes.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.023
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.9770.983

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 head0.027
GPT teacher head0.306
Teacher spread0.278 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2000
Admission routes1
Has abstractyes

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