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Record W4392342931 · doi:10.18806/tesl.v40i1/1384

Multisemiotics, Race, and Academic Literacies

2024· article· en· W4392342931 on OpenAlexaffvenueabout
Pedro dos Santos, Bong-gi Sohn

Bibliographic record

VenueTESL Canada Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of WinnipegSimon Fraser University
Fundersnot available
KeywordsRace (biology)SociologyLinguisticsPsychologyPedagogyGender studiesPhilosophy

Abstract

fetched live from OpenAlex

This study examines the trajectories of two multilingual, racialized academic writing faculty, presenting how we brought our Southern onto-epistemologies (e.g., Santos, 2016) to curriculum, teaching, and assessment. Although plurilingualism has become a significant dimension of Canadian higher education (Marshall, 2020), monolingual norms that emphasize native-like competence continue to be a mainstream discourse in many academic writing courses. Building on the recent raciolinguistic critique (Rosa & Flores, 2017) of the lack of discussion of racism in academic literacies discourse, we acknowledge that academic literacies continues to force plurilingual, international students into a white subject position. Acknowledging the tension between the monolingual ideal and multilingual realities, we explore how two plurilingual, non-white faculty challenge an academic writing tradition that is constructed by the white listening subject. By co-creating duoethnographic narratives that provide insight into our complex biographical journeys as cycles of becoming (Thibault, 2020), our story shows how teaching academic writing is not simply teaching a skillset but involves constant negotiation between students’ and teachers’ lived experiences. Through this process, we conceive of teaching academic literacies as both an ideological construct and a multisemiotic process that involves multiple histories and meaning-making resources across diverse time and place scales.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.040
GPT teacher head0.393
Teacher spread0.353 · 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 designNot applicable
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
Published2024
Admission routes3
Has abstractyes

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