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Record W2060738877 · doi:10.3917/riges.373.0006

Réussir sa carrière : qu'est-ce que cela signifie ?

2012· article· fr· W2060738877 on OpenAlexvenueno aff
Jacqueline Dahan, Yvon Dufour

Bibliographic record

VenueGestion · 2012
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Résumé Au cours des dernières années, l’internationalisation et des changements survenus dans l’environnement économique et démographique ont fortement modifié la trajectoire des carrières en gestion de même que la conception du succès aussi bien au point de vue individuel qu’au point de vue organisationnel. Parallèlement, la crise économique et de nombreuses fraudes financières commises par des dirigeants reconnus ont terni et remis en question notre conception traditionnelle des dirigeants-vedettes tout comme celle de leur succès et de ce que signifie, plus globalement, réussir en tant que gestionnaire. Il apparaît alors important – pour les personnes et pour les dirigeants d’entreprise – de s’interroger sur ce que veut dire le succès en carrière afin de mieux le gérer. Une revue de la documentation nous permet de présenter différents modèles et métaphores du succès professionnel. Cette réflexion sur le succès ainsi que les résultats d’une étude que nous avons menée sur le sujet interpellent non seulement les gestionnaires qui gèrent leur vie professionnelle, mais aussi les organisations qui les embauchent et les écoles de gestion qui les forment. Fonctions : management, GRH Industries : toutes

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)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.019
Scholarly communication0.0100.011
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.003

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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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 designQualitative
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
Published2012
Admission routes1
Has abstractyes

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