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Record W1640731384 · doi:10.1177/160940690500400301

Racism and Ethnocentrism: Social Representations of Preservice Teachers in the Context of Multi- and Intercultural Education

2005· article· en· W1640731384 on OpenAlexaff
Nicole Carignan, Michael Sanders, Roland G. Pourdavood

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2005
Typearticle
Languageen
FieldPsychology
TopicSocial Representations and Identity
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsEthnocentrismMulticulturalismMulticultural educationRacismPerspective (graphical)Context (archaeology)Diversity (politics)PedagogyCultural pluralismSociologyCultural diversityConsciousness raisingSocial constructivismPsychologyTeacher educationSocial psychologyGender studiesAnthropology

Abstract

fetched live from OpenAlex

Using a constructivist inquiry paradigm, the authors attempted in their content analysis to understand the social representations on race and ethnocentrism of preservice secondary teachers studying in an urban university in a Midwest city in the United States. Although social representations can be understood as something in which our participants deeply believe, this study suggests that racial and ethnocentric biases should be examined in the context of multi- and intercultural education. The authors favor a way of revisiting taken-for-granted ideas toward traditional, liberal, and critical or radical multiculturalism. They argue for the recognition not only of the differences and diversity of students (multicultural perspective) but also of the way in which teachers understand, communicate, and interact with them (intercultural perspective).

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)0.009
metaresearch head score (Gemma)0.017
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 score0.010
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0100.024
Scholarly communication0.0080.006
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.419
GPT teacher head0.660
Teacher spread0.241 · 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

Citations48
Published2005
Admission routes1
Has abstractyes

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