Admission Systems and Student Mobility: A Proposal for an EU-Wide Registry for University Admission
Bibliographic record
Abstract
Europe’s higher education systems are struggling to respond to the established mass demand for higher education, especially given the proportional decline in available resources per student and, more generally the demand for an ever longer education and reduction of the population of working age due to demographic decline. In addition, growing student mobility puts pressure on admission systems to set up relevant procedures for applicants who wish to enter a country. Admission systems to higher education constitute one key element in the mitigation of these challenges. Admissions can regulate student flows, and play a key role in guaranteeing the acquisition of skills in higher education by matching student profiles to their desired courses of study. This article puts European admission systems in perspective. The issue of regulation of student mobility is topical, given the broader and salient discussion on migration flows in Europe . The article uses international comparisons with systems such as the US, Australia and Japan, to provide a critical overview of the role of admission systems in an often overlooked but yet fundamental part of the European Higher Education Area, namely student mobility. The paper also argues for the creation of an information-sharing EU registry on admissions practices for mobile students.
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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 | 0.069 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.020 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".