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Record W2948063302

Embodying Difference: A Case for Anti-Racist and Decolonizing Approaches to Multiliteracies

2019· article· en· W2948063302 on OpenAlexaff
Sara Schroeter

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSociologyGender studiesPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This paper asks what pedagogies are needed as Canadians are invited to reconcile colonial pasts with contemporary forms of racism and enduring colonial structures. Sharing discourses of race from youth who participated in a year-long ethnography, and moments from a drama-based pedagogical collaboration, this paper suggests ways of updating multiliteracies frameworks so as to better account for the networks of power that circulate in classrooms. This project had the dual aims of exploring discourses of difference used by students, as well as drama as a multimodal, embodied, and (post)critical pedagogy for unpacking differences embedded in the Grade 9 social studies curriculum. Drawing on feminist pedagogies, critical race studies, and Indigenous critiques of education, the author argues that embodiment and subjectivity are central to teaching and learning, and illustrates through excerpts from interviews and fieldnotes, how race, intersectionality, and White supremacy influence interactions in the classroom. The paper concludes by proposing that multiliteracies and multimodal pedagogies would benefit from centralizing anti-racist and decolonizing approaches to learning, in addition to the networks in which literacy practices occur and through which meaning is made.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.703
GPT teacher head0.616
Teacher spread0.088 · 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.

Study designObservational
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

Citations7
Published2019
Admission routes1
Has abstractyes

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