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Record W2606634956 · doi:10.28945/3397

International Doctoral Students’ Navigations of Identity and Belonging in a Globalizing University

2016· article· en· W2606634956 on OpenAlexaffabout
Jennifer Phelps

Bibliographic record

VenueInternational journal of doctoral studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGlobalizationSociologyAmbiguityTransformative learningNarrativeInternational educationQualitative researchIdentity (music)Construct (python library)PedagogySocial scienceHigher educationPolitical science

Abstract

fetched live from OpenAlex

This article draws on findings from a broad study on the influences of globalization on the experiences of international doctoral students at a large, research intensive Canadian university. It focuses specifically on these students’ lived experiences of change in their national identities and senses of belonging in a globalizing world. Using a qualitative, multiple case narrative approach, students’ experiences were collected via in-depth interview and analyzed through a theoretical lens of transnational social fields. The study found that international doctoral students experienced multiplicity, ambiguity, and flux in their senses of self, belonging, and educational purposes as they engaged in the transnational academic and social spaces of the university. Their narratives are revealing of the ways that international doctoral students consciously construct identities that traverse national affiliations as they engage in higher levels of mobility and interact with highly internationalized environments and networks. The study contributes insight into the transformative nature of international doctoral study and identifies specific ways in which processes of globalization influence the international doctoral student experience.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.419
Teacher spread0.337 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations53
Published2016
Admission routes2
Has abstractyes

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