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Record W2901277671 · doi:10.1522/revueot.v27n1.278

Stimuler la coopération organisationnelle en regard de la diversité internationale des travailleurs : proposition d’un outil de gestion fondé sur la lieuité

2018· article· fr· W2901277671 on OpenAlexaffvenueabout
Carène Tchuinou Tchouwo, Anne‐Laure Saives

Bibliographic record

VenueRevue Organisations & territoires · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsUniversité du Québec à MontréalUniversité Laval
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Cet article vise à comprendre la coopération entre travailleurs immigrants à partir d’une analyse de leurs perceptions du lieu d’implantation de leur organisation. Partant des expériences de quelques travail-leurs camerounais de Montréal, nous avons découvert ce lieu comme une construction aux multiples facettes (géographique, locale et symbolique). Une fois la grille d’analyse « lieuitaire » établie, nous avons entrepris une activité de promenade commentée qui, à partir d’un itinéraire proposé par les participants, nous a permis d’identifier des éléments garants d’une meilleure coopération organisationnelle. Par un intérêt porté au partage des sens du lieu individuels, nous avons découvert que la réalisation d’une telle activité au sein des équipes de travail peut être porteuse d’une plus grande humanisation du travail et permettrait de mieux faire valoir la complémentarité des expériences et des savoirs individuels, des notions clés pour le partage, la création de connaissances, la collaboration et la coopération organisationnelle réussies.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.015
Scholarly communication0.0090.004
Open science0.0010.009
Research integrity0.0020.002
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 head0.045
GPT teacher head0.304
Teacher spread0.259 · 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 designTheoretical or conceptual
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

Citations1
Published2018
Admission routes3
Has abstractyes

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