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Record W4410775268 · doi:10.1080/15505170.2025.2478585

Pedagogy as encounter in the first-year academic writing course at the intersection of race and colonialism

2025· article· en· W4410775268 on OpenAlexaboutno aff
Asher Ghaffar

Bibliographic record

VenueJournal of Curriculum and Pedagogy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsRace (biology)Intersection (aeronautics)ColonialismCourse (navigation)SociologyMathematics educationGender studiesPedagogyPsychologyPolitical scienceEngineeringLaw

Abstract

fetched live from OpenAlex

There is a growing emphasis on shifting conversations about race and colonialism from “safe” to “brave” spaces, aiming to engage students in discussions on race amid global upheaval and neocolonial violence. This social justice pedagogical approach foregrounds students’ lived experiences as sources of knowledge and truth-making. Building on Canadian sociological research, I explore an alternative teaching approach for first-year academic writing classes that emphasizes the relationship between multiracial students’ lived experiences and institutions and disciplinary practices that perpetuate racial inequalities. Drawing on Naeem Inayatullah’s method of deriving pedagogical principles from encounters with students, I analyze key areas where colonial and moral imperatives are embedded in writing curricula. I propose that an effective counter-narrative to dominant pedagogical models involves integrating antiracism as a literacy skill in first-year courses through a pedagogy of encounter. This approach challenges the moral regulation inherent in first-year writing courses, creating a foundation for discussing race and colonialism in a low-stakes environment. Using an antiracist memoir after conducting a class poll, I explore threshold concepts which engage students with race and colonialism in literacy education, while also fostering the transfer of academic writing skills across disciplines, particularly in the social sciences.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.017
GPT teacher head0.405
Teacher spread0.388 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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