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Record W4230361913 · doi:10.1080/00220270110101797

Narrative multiculturalism

2002· article· en· W4230361913 on OpenAlexfundaboutno aff
JoAnn Phillion

Bibliographic record

VenueJournal of Curriculum Studies · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaGovernment of Ontario
KeywordsMulticulturalismMulticultural educationConceptualizationNarrativePedagogySociologyNarrative inquiryPsychologyLinguistics

Abstract

fetched live from OpenAlex

This is the first of three papers based on a 20-month study of teaching and learning in a diverse classroom in a downtown community school in Toronto, Canada. The purpose of the study was to examine teaching and learning in a multicultural classroom and to document successful strategies in working with immigrant and minority students. The three papers detail the process by which this focus on classroom life led to a critique of the literature and to a new way to think about multicultural teaching and learning that I call 'narrative multiculturalism'. In this paper, I explore the place of multiculturalism in education and describe several limitations of the traditional ways of examining the issue. I also outline the understanding with which I began the study and describe the nature of my inquiry. I use my autobiographical experiences of multiculturalism and multicultural research to reflect on the literature of multicultural education. The narrative of my relationship with a teacher participant provides a conceptualization of the field and suggests the nature of narrative multiculturalism.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.013
Scholarly communication0.0080.005
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.200
GPT teacher head0.437
Teacher spread0.237 · 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

Citations50
Published2002
Admission routes2
Has abstractyes

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