Qualitative Research with Former International Students: Reflections on Conceptualization, Planning and Relational Engagement
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.122 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.056 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".