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Linguistic and Cultural Heterogeneity in the Classroom

2005· book-chapter· en· W7101432718 on OpenAlexaffabout

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsVariety (cybernetics)MulticulturalismValue (mathematics)Cultural diversityLinguistic diversityDiversity (politics)PerceptionNeuroscience of multilingualism

Abstract

fetched live from OpenAlex

This chapter attempts to demonstrate the intrinsic interest of Canadian policy and practice concerning ethnolinguistic diversity in the classroom—but also its generalizable value in other settings. It contextualizes matters with a brief discussion of traditional and (slightly) more enlightened assimilative perspectives and pressures, and with an overview of official policies, practices, and perceptions of bilingualism and multiculturalism. It is suggested that there are two important types of language contact at school—that of dialects of the “mainstream” language, whose more standard form is usually the variety reinforced by the school itself, and that of different languages meeting in the classroom. It is also suggested that multiculturalism at school falls into two main categories—the first involves what ought to be general practice in the provision of intercultural sensitivity and awareness (“All education worthy of the name is multicultural”), while the second reflects the particular linguistic and cultural complexities existing beyond the school gates; these are not, of course, watertight compartments. Throughout, the emphasis here is on the need for multiculturalism-at-school to be a seamlessly integrated part of the educational enterprise.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.285
Teacher spread0.229 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2005
Admission routes2
Has abstractyes

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