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Record W3106539500 · doi:10.2307/j.ctv1h0p3gx

Réussir vos projets d’affaires en Afrique

2020· book· fr· W3106539500 on OpenAlexaboutno aff

Bibliographic record

VenuePresses de l'Université Laval eBooks · 2020
Typebook
Languagefr
FieldSocial Sciences
TopicGlobal and Cross-Cultural Management
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Les themes developpes dans ce livre montrent l'interet a faire des affaires en Afrique. Avec exemples et donnees sur les perspectives a l'appui, les differents chapitres amenent de maniere progressive et profonde le lecteur, fut-il entrepreneur ou investisseur, a operer un repositionnement strategique et immediat. Cet ouvrage est aussi concu pour etre un support de cours sur les affaires en Afrique. Ce livre repond aux questions essentielles de la gestion strategique internationale, a savoir?: pourquoi, comment, ou, avec qui et quand faire les affaires en Afrique ? De plus, il aborde de maniere detaillee les questions ayant trait aux caracteristiques de l'environnement des affaires africain, aux occasions d'affaires et aux situations concurrentielles de meme que la gestion des risques, les processus de planification et de gestion de l'exportation, de la sous-traitance internationale, de la co-entreprise internationale, du marketing mix, des ressources humaines, etc. A la suite de ces preoccupations, d'autres aussi importantes et particulieres, comme celle des defis ethiques, de la culture, du financement des projets d'affaires, etc., y sont etudiees. Ce livre est le fruit d'un ensemble d'activites realisees sur l'Afrique par huit membres de la Chaire Stephen-A.-Jarislowsky en gestion des affaires internationales de l'Universite Laval. La Chaire Stephen-A.-Jarislowsky en gestion des affaires internationales a pour role de promouvoir et de soutenir la recherche, la formation et le transfert des connaissances vers les organisations dans le domaine de la gestion internationale.

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)0.012
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.026
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0060.003
Scholarly communication0.0100.004
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0260.007

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 head0.012
GPT teacher head0.230
Teacher spread0.217 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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