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Record W4402524265 · doi:10.5539/ies.v17n5p135

The Great Wall of Australia: Barriers for Chinese International Students in the Australian University Setting

2024· article· en· W4402524265 on OpenAlexvenueno aff
Dennis Lam, Adrian Hale

Bibliographic record

VenueInternational Education Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationHigher educationPedagogySociologyPsychologyPolitical science

Abstract

fetched live from OpenAlex

Australia’s much-vaunted reputation as a successful egalitarian, multicultural country has substantial merit, but it also has a chequered history, and the official narrative of egalitarianism and multiculturalism is experienced differentially by vulnerable, marginalised people and communities who bear the brunt of residual and new forms of racism and linguicism. One of the most vulnerable groups in Australian society is the cohort of Chinese international students, who face barriers of racism, linguicism, and exploitation. This paper presents the results of a study which consulted Chinese international students about their experiences in Australia. It found that while their experiences varied, a disturbing common thread of discrimination - from overt to more clandestine modes of aggression – occurred. Implications for Australian decision-makers are enormous, particularly for educational and governmental institutions, for whom Chinese international students seem to represent a commodity rather than real, often vulnerable, young people.

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)0.007
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 score0.156
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0230.007
Scholarly communication0.0070.003
Open science0.0020.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.069
GPT teacher head0.457
Teacher spread0.388 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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