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Record W647680375 · doi:10.71781/18832

Performance et motivation au travail : une dynamique cyclique?

2012· dissertation· fr· W647680375 on OpenAlexaboutno aff
David Paradis

Bibliographic record

VenueOpen MIND · 2012
Typedissertation
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Le Québec est confronté à un vieillissement de sa population et une pénurie de main-d’œuvre spécialisée. Pour faire face à ces difficultés tout en restant compétitives à l’échelle internationale, les entreprises québécoises doivent adopter des méthodes de gestion novatrices qui génèrent une performance élevée chez leurs travailleurs peu nombreux. Il est donc essentiel de se servir – et de repenser – le mieux possible des déterminants reconnus de la performance au travail, au premier chef : la motivation. Or, les recherches adoptent habituellement une perspective linéaire en un temps entre ces variables, alors que différentes études soulèvent la possibilité que les relations soient différentes lorsque ces variables sont analysées dans une perspective autre que linéaire. La présente étude teste si une dynamique cyclique se produit. Les résultats de médiation multiples ne révèlent pas d’effet significatif de la motivation entre les performances de deux temps de mesure consécutifs. Cela conforte la nécessité d’élargir la recherche sur de nouvelles variables de manière à vérifier le potentiel lien cyclique entre deux performances. Également, cette recherche relève une zone d’ombre dans la relation dynamique entre la motivation et la performance : ce lien n’est pas aussi lisse qu’il ne le semble. Il arrive parfois que les motivations et performances initiales aient un impact plus déterminant sur les comportements futurs.

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)0.003
metaresearch head score (Gemma)0.005
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: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.666

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.198
GPT teacher head0.465
Teacher spread0.268 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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