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Record W2158400250 · doi:10.1177/0261927x07300080

Becoming a Cultural Intermediary

2007· article· en· W2158400250 on OpenAlexaff
Sara Rubenfeld, Richard Clément, Jessica Vinograd, Denise Lussier, Valérie Amireault, Réjean Auger, Monique Lebrun

Bibliographic record

VenueJournal of Language and Social Psychology · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversité du Québec à MontréalMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsPrejudice (legal term)MediationPerspective (graphical)PsychologyContext (archaeology)Social psychologySecond-language acquisitionEthnic groupLinguisticsSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

Much of the research linking language and discrimination has been concerned with first-language practices. Yet an intergroup perspective supports the possibility that prejudice may be communicated between groups not sharing the same first language. This article explores how factors associated with the acquisition and use of a second language contribute to the development of antidiscriminatory behaviours. Data regarding these issues were collected from 209 Francophone university students attending school in a bilingual environment. Two specific goals were pursued: (a) the development of an appropriate intercultural mediation measure and (b) an examination of how factors associated with second-language acquisition relate to the use of antidiscriminatory behaviours. Results demarcate involvement and noninvolvement dimensions of the mediation measure. Furthermore, a path analysis suggests that antidiscriminatory behaviours are linked to identification with one's own ethnic group. Results are discussed within the context of current approaches to the link between language and discrimination.

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)0.005
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.011
Scholarly communication0.0100.007
Open science0.0010.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.003

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 head0.089
GPT teacher head0.555
Teacher spread0.466 · 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

Citations13
Published2007
Admission routes1
Has abstractyes

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Same venueJournal of Language and Social PsychologySame topicMultilingual Education and PolicyFrench-language works237,207