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Systèmes De Gestion Des Ressources Humaines Et Performance Organisationnelle

2004· article· fr· W2017053331 on OpenAlexaffabout
Michel Arcand, Guy Arcand, Mohamed Bayad, Bruno Fabi

Bibliographic record

VenueAnnals of Public and Cooperative Economics · 2004
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Résumé ** : La gestion des ressources humaines et son effet sur la performance de la firme représentent encore de nos jours une zone d’indétermination. Plusieurs théories tentent d’ailleurs d’expliquer cette importante relation. C’est l’approche configurationnelle des ressources humaines qui servira de filtre théorique à cette recherche. Validant cette approche auprès de 46 entreprises financières canadiennes, nous avons démontré, en partie du moins, la robustesse de cette approche stratégique de gestion des ressources humaines. Il ne fait aucun doute qu’une telle approche représente une alternative stratégique intéressante, capable d’accroître l’efficacité de nos firmes dans ce monde économique impitoyable et mondialisé.

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)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.059
GPT teacher head0.242
Teacher spread0.183 · 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 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

Citations5
Published2004
Admission routes2
Has abstractyes

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