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Record W2346005085 · doi:10.1163/17932548-12341318

Politically Sensitive Chinese Students’ Engagement with Democracy in Canada (对政治敏感的中国留学生在加拿大的民主参与:一个案例研究)

2016· article· en· W2346005085 on OpenAlexaffabout
Gang Li

Bibliographic record

VenueJournal of Chinese Overseas · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDemocracyChinaPoliticsCitizenshipPolitical sciencePeople's RepublicSociologyGender studiesPublic administrationLaw

Abstract

fetched live from OpenAlex

Since the late 1990s citizens from the People’s Republic of China have become the largest single group of international students in all major English-speaking countries. However, relatively little has been done to understand the political dimension of Chinese students’ experience in their host societies. Taking Canada as a case country with democracy as a point of reference, this paper explores Chinese international students’ political interactions with Canadian society. Delving into six social-science graduate students’ lived experience with democracy in Metro Vancouver, this paper highlights the fact that some Chinese students tend to become highly sensitive to the political significance and implications of their overseas experience in and through their engagement with democratic discourses and practices in Canada. Furthermore, those students who have obtained Canadian permanent residency or citizenship are even inclined to become fairly active in political life in Canada. 自从二十世纪九十年代末,中华人民共和国的公民已经成为所有主要英语国家最大的国际学生群体。然而,很少有研究关注中国学生留学经历的政治层面。本文选取加拿大为案例国,以民主为参照点,探究中国留学生与加拿大社会之间的互动。此研究的受访对象是六位在大温哥华地区就读不同社会科学专业的研究生。通过深入分析受访对象在加拿大对民主的亲身经历,本文突出强调以下一点,即有些中国留学生会通过他们对民主话语以及民主实践的参与而对其留学经历的政治意义和政治含义变得高度敏感。此外,那些在留学过程中获得加拿大永久居民身份和加拿大公民身份的中国留学生甚至更倾向于在加拿大的政治生活中变得相当活跃。 This article is in Chinese Language

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.010
GPT teacher head0.325
Teacher spread0.314 · 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 designObservational
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

Citations14
Published2016
Admission routes2
Has abstractyes

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