Bibliographic record
Abstract
Abstract Higher education spaces are increasingly becoming sites of immigration management, reflecting the complex interplay between internationalisation and immigration policies within the European Higher Education Area (EHEA). Using the UK as a primary example, this analysis highlights the impact of immigration regulations on universities and international students, illustrating how institutions are increasingly tasked with visa oversight responsibilities. This shift, driven by stringent national policies, has transformed universities into de facto agents of border control, creating ethical dilemmas and administrative burdens while altering the academic ethos. Drawing on qualitative data, the discussion explores the socio-political implications for students, including financial and emotional challenges, as well as broader equity concerns. The analysis extends to other contexts, such as Australia, the US, and Canada, offering a comparative perspective on immigration frameworks and their integration into education systems. Concluding with actionable recommendations, the study advocates for harmonising visa policies within the EHEA, streamlining application processes, and reconsidering the delegation of immigration duties to academic institutions to foster inclusivity and equitable internationalisation.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".