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Record W1517855864 · doi:10.4000/champpenal.620

La gouvernance de la sécurité dans les États faibles et défaillants

2007· article· fr· W1517855864 on OpenAlexaff
Benoît Dupont, Peter Grabosky, Clifford Shearing, Samuel Tanner

Bibliographic record

VenueChamp pénal · 2007
Typearticle
Languagefr
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsInternational Centre for Comparative Criminology
Fundersnot available
KeywordsCorporate governanceState (computer science)State securityBusinessComputer securityPolitical scienceComputer scienceFinanceLawProgramming languagePolitics

Abstract

fetched live from OpenAlex

Cet article vise à identifier des moyens permettant le renforcement des mécanismes de contrôle social et de résolution des conflits dans les États faibles et défaillants. Après avoir examiné la gouvernance de la sécurité telle qu’elle se développe dans les États forts, nous montrons que certaines configurations institutionnelles peuvent être transposées dans un contexte d’État faible, où les institutions étatiques en charge de la sécurité sont défaillantes ou inexistantes. Nous identifions de nouveaux mécanismes de gouvernance de la sécurité qui rendent envisageable un minimum de sécurité humaine dans les États les plus affaiblis.

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)0.004
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.012
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.025
GPT teacher head0.324
Teacher spread0.299 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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