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Record W4386559329 · doi:10.5430/ijhe.v12n5p113

Developing Inventory of The International Education Cooperation for Chinese College Students

2023· article· en· W4386559329 on OpenAlexvenueno aff
Xinyi Ma, Pengfei Chen, Huan Cao

Bibliographic record

VenueInternational Journal of Higher Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsExploratory factor analysisItem analysisReliability (semiconductor)PsychologyExploratory analysisMedical educationInternational educationApplied psychologyHigher educationComputer sciencePolitical scienceMedicinePsychometricsData scienceClinical psychology

Abstract

fetched live from OpenAlex

This study was initially designed to develop and validate the international education cooperation inventory of Chinese college students, by conducting expert review and a survey of 91 Chinese students. In the first phase, an initial pool of 11 items were generated based on concept analysis and a literature review. Moreover, the content was validated and reviewed by international cooperation experts in the field of higher education. The evaluation in the second phase consisted of an item analysis and an exploratory factor analysis. Following the development process, one item was removed due to low discrimination after performing the item analysis, and the questionnaire was finalized with 10 in 2 dimensions, which are overseas and domestic international cooperations with acceptable reliability and validity. It was found that most of the participants completed the questionnaire without difficulty in about 1-2 minutes, it can be claimed that the IEC is an easy-to-use questionnaire that can be applied in future studies.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.451
Teacher spread0.406 · 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 designBench or experimental
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

Citations0
Published2023
Admission routes1
Has abstractyes

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