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Record W4295706263 · doi:10.22329/jtl.v16i2.7002

Rethinking Methodologies: Implications for Research on International Students

2022· article· en· W4295706263 on OpenAlexvenueaboutno aff
Dhruba Neupane

Bibliographic record

VenueJournal of Teaching and Learning · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsForegroundingSociologyLiteracyPaternalismDisadvantagedPedagogyImmigrationEthnographyCompetence (human resources)ColonialismPsychologyPolitical scienceLinguisticsSocial psychology

Abstract

fetched live from OpenAlex

Research in international student success, satisfaction, and challenges seems still to be constructed around the colonial, imperial paradigm. Informed by deficit models of language, culture, and literacy teaching, such research portrays international students’ challenges in terms of deficiency; discounts other languages, cultures, and literacy education; and reinstitutes the progressive and paternalistic role of the West, reifying its linguistic and cultural superiority. This essay interrupts the still dominant narrative that recreates the old binaries in two ways: (a) It frontloads the need to adopt strength-based approaches to counter dominant methodological paradigms from which much of knowledge about culturally and linguistically different/disadvantaged (CLDI) students is derived, and (b) based on my own ethnographic study on a South Asian immigrant population in Canada, it demonstrates that what the old paradigm views as deficits can and should be the very measures from which to appraise student success and satisfaction. Accordingly, the article’s main objectives are twofold: (a) expose the weaknesses of the deficit models of language, culture, and competence and (b) stress the need to reshape international student studies in higher education as a field of inquiry by foregrounding appreciative models and methodologies.

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)0.017
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.320
GPT teacher head0.480
Teacher spread0.160 · 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
Published2022
Admission routes2
Has abstractyes

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