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Record W4386754113 · doi:10.31468/dwr.1033

Higher Education Internationalization and English Language Instruction. Xiangying Huo. Springer, 2020

2023· article· en· W4386754113 on OpenAlexaffvenueabout
Qinghua Chen

Bibliographic record

VenueDiscourse and Writing/Rédactologie · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsInternationalizationSociologyHigher educationMulticulturalismPedagogyNarrativeIntersectionalityInternationalization of Higher EducationAutoethnographyGender studiesLinguisticsPolitical science

Abstract

fetched live from OpenAlex

The book "Higher Education Internationalization and English Language Instruction" is an autoethnographic work that examines the intersectionality of race and language in the Canadian higher education system. Through personal stories and narratives, the author explores themes such as native-speakerism, writing centre tutoring, multicultural education, and social justice. The book makes two significant contributions: first, it amplifies the voice of racialized individuals through the application of Critical Race Theory to personal experiences and diaries, serving as a springboard for thought and an invitation to dialogues on transformation. Second, it demonstrates the potential of personal narratives to reveal ideas that are often overlooked in positivist approaches, providing insight into methodological approaches that graduate students and young researchers can adopt. The book concludes with practical implications for addressing discriminatory systems and practises in universities to promote diversity and inclusiveness. The book follows a standard format for scholarly works and provides a useful background on the internationalisation of higher education and the significance of English as a medium for multiculturalism in Canada.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.061
GPT teacher head0.355
Teacher spread0.294 · 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.

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

Citations0
Published2023
Admission routes3
Has abstractyes

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