Discursive (mis)alignments in internationalization: The case of International Medical Programmes
Bibliographic record
Abstract
PURPOSE: International Medical Programmes (IMPs) form a distinctive modality in medical education, with diverse student populations, English as a language of instruction and 'globalized' curricula. A lack of common understanding of IMPs' purposes and role in the medical education landscape triggers critiques. This study aims to document the effects of different discourses used to justify the purpose of IMPs. METHODS: We use a discourse analysis approach to explore the different ways in which the purposes of IMPs are constructed at the regulatory, institutional, and individual level, and how these discourses interact. The research situates in two IMPs, in the Netherlands and in Hungary. Key-informant interviews, policy documents, and scholarly literature form the archive. RESULTS: The purpose of IMPs is constructed discursively around three distinct narratives and associated practices: around serving the institutions that host them, around serving the (global) public interest, and around serving individual students. Co-existence and misalignments of these three discourses cause conflicting practices and confusion among stakeholders. CONCLUSIONS: This study illustrates how diverging perspectives on internationalization in medical education create tensions for learners and staff. Articulating a clear and explicit meaning to internationalization may reduce uncertainties, and may reinforce realistic expectations of what constitutes a good outcome.
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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 | 0.034 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.025 | 0.078 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".