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Le suivi de la performance, de I'incantatoire au passage à I'acte:Une étude du suivi de performance de six programmes d'aide aux entreprises1

2002· article· fr· W2051987619 on OpenAlexaff
Jacques Bourgault, Isabelle Marsolais

Bibliographic record

VenueCanadian Public Administration · 2002
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsCentre de Géomatique du Québec
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Sommaire: Cet article étudie le suivi de la performance dans les programmes publics et exprime le potentiel que représente cet outil de gestion pour les organisations publiques. Les auteurs traitent d'une question de recherche portant sur la conformité du suivi de la performance dans les programmes sous étude pour la période 1993–1998, et des causes des écarts observés par rapport au modèle‐type de suivi de la performance. Le suivi de performance fut réaliséà cette époque de manière ad hoc et opportuniste. Leurs recherches ont permis de conclure que le suivi de performance s'est heurté d'abord à la difficulté de définir de manière univoque le concept de la « bonne » performance d'un programme. S'ensuivirent des difficultés intellectuelles et pratiques pour mesurer et évaluer ladite performance. Cependant, la difficulté de mettre en place un réel processus de suivi n'est pas imputée à cette seule cause. On observerait plutôt la convergence de plusieurs causes qui se manifestent en efforts conceptuels insuffisants, au moment de la conception et de la mise en Deuvre des programmes, pour développer et implanter un authentique suivi de la performance. Abstract: This article focuses on performance monitoring in public programs and demonstrates the potential this management tool has for public organizations. The authors examine studies dealing with the consistency of performance monitoring during the period 1993 to 1998 for the programs under review, and the reasons for the differences observed when compared with the standard model for performance monitoring. During this period, performance monitoring was done on an ad hoc and opportunistic basis. Based on their research, the authors conclude that the first obstacle to performance monitoring is the difficulty of defining, unequivocally, the concept of “good” performance with reference to a program, as well as the intellectual and practical difficulties of measuring and evaluating the said performance. However, this is not the only reason why implementing a real monitoring process is so difficult. Rather, it could be that the convergence of several causes at the time the programs are designed and implemented, which results in insufficient conceptual efforts to develop and establish an authentic performance monitoring process.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.262
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 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
Published2002
Admission routes1
Has abstractyes

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