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Record W2227558343

Utilisation d'un "balanced scoreboard" pour l'intégration des mesures associées à un programme d'assurance qualité

2004· article· fr· W2227558343 on OpenAlexaff
Sylvain Béland, Alain Abran

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsQuality assurancePolitical scienceHumanitiesEngineeringPhilosophyOperations management
DOInot available

Abstract

fetched live from OpenAlex

Pour mettre en place un programme d’assurance qualite de maniere efficace dans une organisation qui developpe, maintient et utilise intensivement du logiciel (SIO Software intensive organisations), l’organisation doit necessairement mettre en place un programme de mesures. Bien qu'il y ait dans la litterature de multiples mesures proposees pour des plans d'assurance de qualite et de gestion de la qualite, il n'existe pas de cadres integrateurs pour ces mesures. Nous proposons ici l'utilisation du Balanced Scoreboard (BSC) comme cadre multidimensionnel pour decrire, implanter et maintenir la strategie de qualite a tous les niveaux de l’organisation, en liant au travers d’une structure logique les objectifs, les indicateurs et les mesures..

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)0.011
metaresearch head score (Gemma)0.030
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: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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 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

Citations2
Published2004
Admission routes1
Has abstractyes

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