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Record W3159246173 · doi:10.5539/ass.v17n5p1

Developing a Framework to Explore Local Researchers’ Engagement with Global Academia: The Case of Vietnamese Social Sciences Scholars

2021· article· en· W3159246173 on OpenAlexvenueno aff
Cuong Huu Hoang, Trang Thi Doan Dang

Bibliographic record

VenueAsian Social Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsnot available
FundersMonash University
KeywordsVietnameseDisadvantagedContext (archaeology)Community engagementPublic relationsPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

In recent years, local scholars have been playing an increasingly significant role in the global knowledge system. However, in the context of Vietnam, interaction and engagement between Vietnamese social sciences researchers (VSSRs) with the global academic world are limited despite efforts from the Vietnamese government and tertiary institutions. This study explores the barriers that prevent Vietnamese scholars engaging with the international academic community. Eighty-two Vietnamese scholars in various fields of social sciences responded to an online self-reporting questionnaire including 13 closed-ended and nine open-ended questions. The results show that various individual factors (e.g., the researchers’ inadequate proficiency in English or limited research capacities), organisational factors (e.g., the lack of a supportive research environment, the lack of funding and resources, and unsupportive policies), and broader factors (e.g., political censors or the tradition of social research) could significantly influence VSSRs’ engagement with global academia. The study underlines the need for in-depth scholar-centred research to understand the process in which local researchers, who are disadvantaged by their contextual factors, participate in the international academic community. More importantly, findings are used to develop a potential framework to study local researchers’ academic engagement with global academia.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.006
Science and technology studies0.0200.011
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.174
GPT teacher head0.442
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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