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Record W3158575013 · doi:10.5430/ijhe.v10n5p88

International Doctoral Student Research Self-Efficacy Scandal: A Case Study of Chinese University

2021· article· en· W3158575013 on OpenAlexvenueno aff
Sabika Khalid, Endale Tadesse, Mohamed Leghdaf Abdellahi, Chunhai Gao

Bibliographic record

VenueInternational Journal of Higher Education · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsInternationalizationChinaInternationalization of Higher EducationInternational educationStudy abroadGlobalizationCultural exchangePolitical scienceHigher educationPedagogySociologyPsychologyMedical educationPublic relationsMedicineBusinessLaw

Abstract

fetched live from OpenAlex

The internationalization of Chinese higher education glimmers the hope for globalization and opened the doors for countries to exchange academics treasures and cultural exchange. The euphonious and mellifluous agenda behind the internationalization led the nations towards the silver lining of collaboration, interaction, and human resources exchange. However, a large volume of literature claimed the poor academic performance of international students and weak academic communication between international students and Chinese faculty members. So the study sought to explore the phenomena through doctoral. Student experience regarding their research self-efficacy development in a Chinese university. Thus, we took one Chinese university as a case due to its high accessibility of international students with broad and deep experience of being an international student in China. Our participants were international doctoral students from Asian and African countries with significant financial desperation. The findings shed light on the intention of international to pursue a doctoral degree in Chinese higher education and how it significantly delayed their research self-efficacy development.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.289
GPT teacher head0.618
Teacher spread0.329 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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