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Record W2795758899 · doi:10.1163/22125868-12340083

“I’m not an ESL teacher and I’m not trained to do it”

2017· article· en· W2795758899 on OpenAlexaffabout
Lorin G. Yochim, Laura Servage

Bibliographic record

VenueInternational Journal of Chinese Education · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNegotiationAdaptation (eye)NeglectDisciplinePedagogyPsychologyLearning stylesWork (physics)Mathematics educationSociologySocial science

Abstract

fetched live from OpenAlex

This article discusses how teaching faculty in a western Canadian university respond to the growing number of Chinese international students in their classrooms. Interviews (n=21) and survey data (n=60) reveal that professors struggle to communicate academic expectations across language and cultural barriers; develop cross-cultural content; engage students in active learning in the classroom; and provide effective feedback on written work. This in-depth account shows how faculty negotiate demands to both adapt to and create an “internationalized classroom” in the absence of institutional supports. Unsurprisingly, we confirm that adaptation is a struggle. Faculty rely on a combination of personal experience, disciplinary grounding, and stereotyping to inform their efforts. We conclude with a discussion of the limited utility of “Confucian Heritage Culture” ( chc ) as a path to meaningful change. Though frequently invoked to describe the preferences and behaviors of Chinese students, the homogenizing and misleading assumptions of the chc framework prevent faculty from recognizing the contemporary reality of these students’ country of origin and leads them to neglect individual student learning styles and needs.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.188
Threshold uncertainty score0.374

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.011
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.029
GPT teacher head0.347
Teacher spread0.318 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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