MétaCan
Menu
Back to cohort
Record W4292575423 · doi:10.1177/18681026221110245

Educated into Sinophilia? How Kazakh Graduates/Students of Chinese Universities Perceive China

2022· article· en· W4292575423 on OpenAlexfundno aff
Zhanibek Arynov

Bibliographic record

VenueJournal of Current Chinese Affairs · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Ethnic Minorities and Relations
Canadian institutionsnot available
FundersNazarbayev UniversityUniversity of Toronto
KeywordsKazakhChinaAlienationPopulationPolitical sciencePerceptionChinese peopleEconomic growthSociologyPsychologyLawDemographyEconomics

Abstract

fetched live from OpenAlex

This article examines perceptions of China and contributes to the ongoing academic debate on Sinophobia in Central Asia. However, unlike existing studies, it specifically focuses on perceptions of those, who have first-hand China experience – Kazakh students/graduates of Chinese universities. Based on in-depth interviews with them, the article argues that those with first-hand China experience tend to reject the China threat theory, found to be widespread among the general population. Instead, China-educated Kazakh youth perceive China mostly as an economic opportunity for their own country. Yet, this does not necessarily make them Sinophiles in the sense that they still express certain concerns related to their country’s potential over-dependence on China. But more interestingly, they see China as the “civilizational other.” This perceived civilisational abyss even among the more-informed segments of the population appears to be one of the main causes of the alienation of China and the Chinese in Kazakhstan.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.334
Teacher spread0.319 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Current Chinese AffairsSame topicChina's Ethnic Minorities and RelationsFrench-language works237,207