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Record W2046275796 · doi:10.7202/006551ar

À la recherche d’une évaluation « pauvre »

2003· article· fr· W2046275796 on OpenAlexaffvenue
Jean‐Paul Brodeur

Bibliographic record

VenueCriminologie · 2003
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité de MontréalInternational Centre for Comparative Criminology
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cet article développe l’argumentaire suivant : dans un grand nombre de contextes d’évaluation, les conditions ne permettent pas qu’on procède à des évaluations des modalités et des effets de l’intervention policière qui respectent toutes les règles d’une méthodologie forte. Il faut en conséquence recourir à des évaluations méthodologiquement moins orthodoxes, qui n’en conservent pas moins leur utilité et leur valeur heuristique. La première partie de l’article est consacrée à l’objet et aux méthodes de l’évaluation. En se fondant sur les notions développées dans cette première partie, la seconde indique quelles sont les difficultés que soulève, dans un contexte pratique d’intervention, la mise en place d’un cadre d’évaluation qui respecte pleinement les caractères de l’objet d’une évaluation et les exigences d’une méthode rigoureuse, l’une de ces difficultés étant les coûts prohibitifs engendrés par la réalisation d’une évaluation riche. Dans une troisième partie, nous présentons une série de procédés qui peuvent conduire à des évaluations qui satisfassent autant aux exigences de la théorie qu’à celles de la pratique de l’intervention policière. En conclusion, nous discutons brièvement des résultats des évaluations jusqu’ici effectuées de la police de communauté et de la police de résolution de problème.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.083
metaresearch head score (Gemma)0.183
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: Editorial · Consensus signal: none
Teacher disagreement score0.083
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.183
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0040.018
Scholarly communication0.0180.019
Open science0.0030.007
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0190.004

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 head0.975
GPT teacher head0.638
Teacher spread0.337 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical or conceptual
Domainnot available
GenreEmpirical · Commentary

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

Citations4
Published2003
Admission routes2
Has abstractyes

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