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Record W4372046505 · doi:10.46743/2160-3715/2023.6067

Qualitative Research with Former International Students: Reflections on Conceptualization, Planning and Relational Engagement

2022· article· en· W4372046505 on OpenAlexafffund
Jon Woodend, Nancy Arthur

Bibliographic record

VenueThe Qualitative Report · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsConceptualizationQualitative researchInternational educationSociologyStudy abroadPublic relationsStudent engagementPopulationPedagogyPsychologyPolitical scienceHigher educationSocial science

Abstract

fetched live from OpenAlex

The number of international students seeking a foreign education, particularly in Westernized countries, has grown dramatically over the past decade, and is predicted to continue to increase, despite a period of disruption due to COVID-19. Given this growth, there is a significant body of research on key insights into the initial transition experiences, both academic and personal, of international students to the host country, with a developing body of research exploring their post-study transition. Understanding these post-study transitions is important in creating policy and services that appropriately support international students. Due to the diverse and sometimes complex post-study pathways of former international students, accessing this population to conduct qualitative research can create challenges for researchers. To help address these challenges, the authors highlight three critical considerations based on their qualitative research experiences in Westernized countries with former international students, including conceptual understandings, logistical planning, and relational engagement. Moreover, the authors share examples of pragmatic solutions related to challenges with conceptual understandings, logistical planning, and relational engagement in qualitative research with former international students. The purpose of this article is to start and invite discussion around how best to reach, access, and work with former international students to expand qualitative research on the post-study 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)0.017
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.584
GPT teacher head0.664
Teacher spread0.080 · 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 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

Citations4
Published2022
Admission routes2
Has abstractyes

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