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Record W2805467866 · doi:10.46743/2160-3715/2018.3098

Critical Reflections in International Contexts: PolyEthnographic Accounts of an International Doctoral Research Seminar

2018· article· en· W2805467866 on OpenAlexaffabout
Lisa Fedoruk, Jon Woodend, Janet Groen, Avis Beek, Sylvie Roy, Xueqin Wu, Li Xiang

Bibliographic record

VenueThe Qualitative Report · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeneral partnershipSociologyInternational educationChinaPower (physics)PedagogyMeaning (existential)SalientInternational studiesHigher educationPolitical scienceSocial sciencePsychology

Abstract

fetched live from OpenAlex

As the world becomes more globally interconnected, international partnerships, including those within higher education, have increased. In an exemplar of these international partnerships from an academic standpoint, selected doctoral students and faculty from Australian, Chinese, and Canadian universities participated in an International Doctoral Research Seminar held in China in December 2015. The objective of this seminar was to have academic debate regarding educational reform. A critical by-product of this seminar was the meaning made by the participants from this experience. This paper reviews the critical polyethnographic reflections of the Canadian participants for three salient and influential topics including the role of culture, power dynamics, and organizational systems, all in relation to this international academic partnership experience. These reflections have ramifications for future programs specifically for enhancing the international development of doctoral students under the broader umbrella of international academic partnerships.

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)0.019
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.044
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0350.032
Scholarly communication0.0100.006
Open science0.0030.015
Research integrity0.0040.012
Insufficient payload (model declined to judge)0.0050.001

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 head0.501
GPT teacher head0.692
Teacher spread0.191 · 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.

Study designQualitative
DomainMethods
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
Published2018
Admission routes2
Has abstractyes

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