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Record W4363609112 · doi:10.29140/mle.v1n2.383

Towards sustainable internationalization in post-COVID higher education: Voices from non-native English-speaking international students in Canada

2020· article· en· W4363609112 on OpenAlexaboutno aff
P.A. King

Bibliographic record

VenueMigration and Language Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Internationalization2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Internationalization of Higher EducationPolitical scienceHigher educationInternational educationSociologyPedagogyBusinessMedicineInternational tradeVirology

Abstract

fetched live from OpenAlex

International students have been a dominant topic in Canadian government and institutional strategies in recent years (Tamtik, 2017; Trilokekar & El Masri, 2020). As of 2019, the Canadian Bureau of International Education (CBIE) reports over 600,000 international students in Canada across all levels of study. In the midst of the COVID-19 pandemic, the complexities around student engagement, learning and community building are complicated by remote learning. International students, in particular, face increasing challenges due to isolation from home countries and a reduction of in-person support services. We must now, more than ever, identify and address the lack of supports available to international students. This qualitative study provides voices from non-native English-speaking international students in Ontario universities speaking about the institutional support systems they have experienced in Canada. The findings are reported in a narrative format.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.013
GPT teacher head0.325
Teacher spread0.312 · 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 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

Citations13
Published2020
Admission routes1
Has abstractyes

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