MétaCan
Menu
Back to cohort
Record W1983257366 · doi:10.7202/1020649ar

La justice, l’efficacité et l’imputabilité

2013· article· fr· W1983257366 on OpenAlexvenueno aff
Daniel Mockle

Bibliographic record

VenueLes Cahiers de droit · 2013
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Les principes issus de la nouvelle gestion publique (efficacité, efficience, transparence, responsabilité et imputabilité) sont utilisés par le législateur afin de revoir sur une base plus contemporaine le fonctionnement des organisations publiques. Leur intégration par le droit public montre une transformation du contexte social et politique qui dépasse les dimensions propres à la gestion et au management. La justice, à titre de système public propre à l’organisation et au fonctionnement des cours et des tribunaux, subit l’influence, de diverses manières, de cette évolution et de ce type de principe. Ce texte montre la progression de l’efficacité en droit processuel, ainsi que dans le contexte du nouveau management de la justice. Si, au sens strict, l’efficacité n’est pas un principe reconnu par le droit judiciaire, elle apparaît à tout le moins comme un élément structurant des réflexions contemporaines sur la justice, à l’instar de l’accessibilité. L’efficacité concerne directement les cours et les tribunaux en tant qu’organisations responsables du service public de la justice. Son ascension facilite ainsi l’affirmation des autres principes, dont le plus important reste l’imputabilité. Loin d’être en opposition avec les principes traditionnels de l’indépendance de la justice, l’imputabilité ouvre des perspectives nouvelles pour sa légitimité.

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)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0070.041
Scholarly communication0.0180.009
Open science0.0020.006
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0060.002

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 head0.038
GPT teacher head0.390
Teacher spread0.352 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueLes Cahiers de droitSame topicHealthcare Systems and PracticesFrench-language works237,207