MétaCan
Menu
Back to cohort
Record W2219248941

La gestion des risques d'un projet de développement et d'implantation d'un système informatisé au Ministère de la Justice du Québec

2008· article· fr· W2219248941 on OpenAlexaboutno aff
Claude Y. Laporte, Denis Roy, Rosalia Novieli

Bibliographic record

VenueGénie logiciel · 2008
Typearticle
Languagefr
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

En 1999, le Ministere de la Justice du Quebec a decide de regrouper les activites de gestion des infractions et de perception des amendes. Plus de 700 000 dossiers sont traites et 110 millions de dollars sont percus a chaque. On avait note une augmentation des comptes a recevoir et un flechissement des recettes. Ces secteurs d'activite etaient supportes au niveau des operations par deux systemes informatiques Le systeme de la gestion des infractions concu au debut des annees 1990, supportait l'activite de la gestion des infractions. Le systeme du controle des revenus, concu en 1983, etait utilise par les percepteurs pour faire le suivi du paiement des amendes. Ce projet comportait le developpement d'un nouveau systeme, soit le systeme de gestion des infractions et de perception des amendes, afin d'assister les activites du Bureau des infractions et amendes. Ce projet a permis une economie globale nette de 46,7 MS soit une baisse des couts de 35,9%. Ce projet a recu plusieurs prix d'excellence. Dans cet article on decrit les principales phases du projet, les activites d'evaluation et de gestion des risques une analyse financiere, le bilan de projet et des recommandations pour un projet futur. La gestion des risques a contribue de facon significative au succes du projet.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.064
GPT teacher head0.349
Teacher spread0.286 · 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 teacher head, not a consensus.

Study designObservational
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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueGénie logicielSame topicConstruction Project Management and PerformanceFrench-language works237,207