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Record W4377091361 · doi:10.1590/1678-460x202356227

Trans/plurilingual Pedagogies: A Multiethnography

2023· preprint· en· W4377091361 on OpenAlexaffabout
James Corcoran, Brian Morgan, Jacqueline Ng, Heejin Song, Marlon Valencia

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsYork University
Fundersnot available
KeywordsAffordanceMultilingualismPedagogySociologyEnglish languageApplied linguisticsMathematics educationLinguisticsPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Trans/plurilingual theory and pedagogies have been generating extensive attention within global English language teaching and teacher education. This article responds to the burgeoning area of trans/plurilingual pedagogies, outlining diverse pedagogical practices and perspectives from a group of English language educators at a large, cosmopolitan Canadian university. Considering recent assertions that there are onto-epistemological differences between multilingualism, plurilingualism, and translingualism, this article looks to demonstrate how (or even if) these differences manifest in the pedagogical practices of diverse faculty within the same language teaching and teacher education programs. Drawing on multiethnographic data, this article concludes with a discussion of the potential and limitations of critical pedagogies, the affordances of multiethnography as an accessible methodology for use by researchers and pedagogues, and a call for greater bi-directional knowledge flow between language researchers and classroom instructors.

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)0.011
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.012
Scholarly communication0.0050.005
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.139
GPT teacher head0.321
Teacher spread0.182 · 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 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
Published2023
Admission routes2
Has abstractyes

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