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Record W4297900986 · doi:10.1002/tesq.3190

The Accent Work of International Teaching Assistants

2022· article· en· W4297900986 on OpenAlexaffabout
Vijay A. Ramjattan

Bibliographic record

VenueTESOL Quarterly · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStress (linguistics)Work (physics)NarrativeSet (abstract data type)PsychologyLinguisticsPedagogySociologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract “Accent work” is a term used to describe the preparatory work that professional actors undergo to attain a speech accent for a role. However, this article presents international teaching assistants (ITAs) as another set of workers who engage in their own type of accent work. Since the accents of ITAs are constructed as “liabilities” for communication‐based tasks in English‐medium universities in the Global North, their accent work entails ensuring that their speech is not a professional interference. Drawing on a narrative study done with 14 ITAs working in Ontario, Canada, the article details how the accent work of these ITAs consisted of working on or around their accents, which meant modifying accents to various degrees or engaging in tasks that did not require accent modification, respectively. These two practices of accent work were influenced by factors such as academic discipline, social locations, and audience. To conclude, the article offers preliminary recommendations on how accent work can particularly inform ITA training.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.004
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.016
GPT teacher head0.231
Teacher spread0.216 · 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

Citations50
Published2022
Admission routes2
Has abstractyes

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