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Record W2134231976 · doi:10.18251/ijme.v12i1.248

Moving Beyond “Dance, Dress, and Dining” in Multicultural Canada

2010· article· en· W2134231976 on OpenAlexaffabout
Özlem Sensoy, Raj Sanghera, Geetu Parmar, Nisha Parhar, Lianne Nosyk, Monica Anderson

Bibliographic record

VenueInternational Journal of Multicultural Education · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDanceMainstreamMulticulturalismDiversity (politics)Ethnic groupCurriculumGovernment (linguistics)SociologyMulticultural educationMedia studiesPedagogyPolitical scienceVisual artsPublic relationsAnthropologyArtLaw

Abstract

fetched live from OpenAlex

The mainstream curriculum is extremely efficient in furthering a neoliberal multicultural discourse, what is described as the dance, dress, and dining, or heroes and holidays, or Taco Tuesday approaches to diversity. Given this, doing anything else is an ongoing challenge. This paper shares details of a government-university-school collaboration in which the authors were participants in a national project to digitize ethnic group archives and create lesson plans based from those archives. The authors share insights and challenges about what worked for resisting the “zoo” approach.

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)0.002
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 score0.059
Threshold uncertainty score0.425

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0280.008
Scholarly communication0.0050.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.026
GPT teacher head0.366
Teacher spread0.341 · 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

Citations15
Published2010
Admission routes2
Has abstractyes

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