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A Textbook Case of Antiracism

2024· book-chapter· en· W4390711862 on OpenAlexaffabout
Srividya Natarajan, Emily Pez

Bibliographic record

VenueAdvances in educational marketing, administration, and leadership book series · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsThe King's University
Fundersnot available
KeywordsArticulation (sociology)InstitutionPedagogySociologyLinguisticsMathematics educationPsychologyPolitical scienceSocial sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

The pedagogic assumption that English is not only a target language for international students and other L2 English users, but also a metonym for the desirable culture to which they must assimilate is still prevalent in many Canadian institutions. This chapter discusses how two teacher-practitioners wrote a first-year writing (FYW) textbook for multilingual students, drawing on critical pedagogy to resist this form of white linguistic and epistemic supremacy while also empowering multilingual writers and resolving the vexed question of content in writing textbooks. In this chapter, the authors describe their fruitless search for a suitable textbook, their decision to write their own, their articulation of the principles that would guide their composing process, the frameworks they drew upon, and the secondary research that supported their choices as they created FYW learning materials that were antiracist and anti-linguicist but supportive of the academic success of multilingual students within the prevailing assessment ecologies in their institution.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.007
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.002

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 head0.078
GPT teacher head0.411
Teacher spread0.333 · 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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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