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Record W4409171103 · doi:10.1080/09518398.2025.2470934

A multi-ethnography: reflections on the education and training of graduate studies in Vietnam

2025· article· en· W4409171103 on OpenAlexaffabout
Nguyen Thi Thinh, Giang Nguyen Hoang Le, Vu A. Le

Bibliographic record

VenueInternational Journal of Qualitative Studies in Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsEthnographyTraining (meteorology)SociologyPedagogyQualitative researchGraduate studentsGraduate educationHigher educationPsychologyMedical educationGender studiesMathematics educationPolitical scienceAnthropologyGeographyMedicine

Abstract

fetched live from OpenAlex

Drawing on meritocracy and Foucault’s theory of power/knowledge, we discuss the construct, merited people, in Vietnamese society. This multi-ethnographic study represents our dialogues as PhD and master’s degree holders in Vietnam where power/knowledge is understood through the dominant role of higher education institutions (HEIs). Our dialogues feature a Vietnamese returnee from Canada grappling with bringing unfamiliar knowledge of gender and sex education into a Vietnamese higher education context and an individual’s skepticism of one’s knowledge production and transformation in their doctoral pursuit. Other narratives emerge from a doctoral degree holder’s feelings of uncertainty about his scholarship and research competency due to his institution’s negative publicity and a master’s degree graduate who is concerned about lacking a legitimate assessment of merit in Vietnam academia. Our study makes an original contribution to the understanding of knowledge production governed by power relations in HEIs and its emerging research culture.

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)0.008
metaresearch head score (Gemma)0.012
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0200.015
Scholarly communication0.0070.006
Open science0.0020.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.546
GPT teacher head0.629
Teacher spread0.083 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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