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Record W1805520176 · doi:10.3917/resg.106.0091

La gestion des ressources humaines et le problème de l’encastrement de l’entreprise en Afrique : l’exemple d’une mine au Ghana

2015· article· fr· W1805520176 on OpenAlexaff
Armel Brice Adanhounme

Bibliographic record

VenueRecherches en Sciences de Gestion · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicGlobal and Cross-Cultural Management
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

À rebours de l’approche normative courante, cet article propose une analyse micro-politique institutionnelle pour discuter des enjeux socioculturels de la GRH à l’épreuve du contexte africain. La contestation de la stratégie managériale fait ressortir deux modèles de GRH dans une mine au Ghana, filiale d’une multinationale nordaméricaine. Façonné par les politiques de restructuration, le premier modèle est orienté vers la RSE et s’oppose au second plutôt focalisé sur les rapports collectifs d’emploi. Mais allant au-delà de l’opposition entre les travailleurs et la communauté qui segmente les pratiques de GRH, l’article recourt aux mécanismes institutionnels pour expliquer comment les acteurs locaux négocient à leur convenance l’encastrement de l’entreprise dans leur contexte.

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)0.003
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 score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.012
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.397
Teacher spread0.199 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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