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Record W2609424587 · doi:10.7202/1034512ar

Le jeu des identités culturelles dans les relations interethniques et intra-ethniques chez les migrants

2016· article· fr· W2609424587 on OpenAlexvenueno aff
Pierre Mannoni, Nicole Barthe

Bibliographic record

VenueInternational Review of Community Development · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Le phénomène de la migration est l’un des principaux cas de confrontation de groupes humains d’origines culturelles diverses. Ce fait impose des ajustements permettant, notamment aux migrants, de s’intégrer dans la société d’accueil avec le plus d’efficacité et de rapidité. Les individus concernés sont astreints, pour répondre à ces nécessités d’adaptation, à un remaniement psychologique plus ou moins important et plus ou moins réussi. Car les relations interculturelles supposent toujours des situations de compromis entre deux (ou plusieurs) cultures et le résultat n’est pas acquis d’avance. On peut distinguer finalement quatre grands cas de figures suivant que l’on a affaire à des groupes culturellement hétérogènes (relations interculturelles) ou à des groupes culturellement homogènes (relations intra-culturelles), et que ces rapports vont dans le sens de la réduction des différences ou, au contraire, de leur maintien, voire de leur exagération. Ce qui débouche sur quatre situations assez nettement distinctes :

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)0.009
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.013
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.088
GPT teacher head0.410
Teacher spread0.323 · 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
Published2016
Admission routes1
Has abstractyes

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Same venueInternational Review of Community DevelopmentSame topicFrench Language Learning MethodsFrench-language works237,207