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Empowering Linguistic Diversity

2024· book-chapter· en· W4390711834 on OpenAlexaffabout
Anita Chaudhuri, Jordan Stouck

Bibliographic record

VenueAdvances in educational marketing, administration, and leadership book series · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPedagogyDialogicCurriculumSociologyLinguistic diversityDiversity (politics)NegotiationRacismEconomic JusticeMultilingualismLinguisticsPolitical scienceGender studiesSocial science

Abstract

fetched live from OpenAlex

The chapter uses the case of multilingual students to discuss how teaching and learning practices in Canadian writing classrooms must examine “systems and structures of linguicism, racism, and classism, which are interrelated and continuously shaping one another'' to develop an understanding of linguistic racism. A critical dialogic approach was used to listen to the study participants and explore strategies to promote decolonial practice in the writing classroom and inform literature on Canadian multilingual pedagogy. The chapter identifies themes of diversity, curriculum design and instructional practice aligned with linguistic justice practices, and perceptions of success and challenges to recommend theoretical standpoints and examples of classroom practice. Through this process of negotiating theory into practice, the authors move from a focus on linguistically and culturally responsive pedagogy toward sustaining and revitalizing pedagogy. They conclude with macro-level strategies and a call to promote and sustain linguistic justice.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other
Teacher disagreement score0.117
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0290.023
Scholarly communication0.0100.006
Open science0.0020.017
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.082
GPT teacher head0.407
Teacher spread0.326 · 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 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
Published2024
Admission routes2
Has abstractyes

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