Student Mobility and the Struggle over European Intergration: The Lessons of the Bologna Process
Bibliographic record
Abstract
This study aims to provide insight about the dynamics of credit and degree mobile students in the European Union (EU). The analysis allows for cross-country comparisons and helps to assess the evolution of student mobility rates in the studied countries. The researcher analyzes data for tertiary mobile enrolments (both sexes). The study draws on the Leuven and Louvain-la-Neuve communiqué of 2009 as a turning point which shifted to quantitative targets. First, using Statistical Product and Service Solutions (SPSS), the researcher conducts statistical analysis by implementing a paired comparisons test. It tests a hypothesis about the mean of the differences (µd) that can be observed across the EU member-states over period from 1999 to 2018. Second, using the European Tertiary Education Register (ETER) data from 2011/2012 to 2016/2017, the researcher contributes to the construction of degree and credit mobility indicators (benchmarks). In brief, it uses ETER database to derive figures that are necessary for computing ratios such as “Share of degree mobile students” and “Share of incoming Erasmus students”. In this study the unit of analysis is the country.Another focus of the study is on tensions and contradictions that arise due to situations where students, on the one hand, are desired to fulfill the needs of skilled labor market; and, on the other hand, the very same students are “unwanted” due to politics of migration control. This is very typical that different actors hold different positions; therefore, employers want more international workers and local employees want fewer international workers. In particular, the researcher addresses the experience of Austria, Estonia, Germany, Sweden, and the UK. The study is concerned with the role of states in initiating, selecting, restraining, and ending international migration movement. It shows how institutions and governments in countries with well-developed higher education (HE) systems are creating initiatives to receive students from abroad. Within the Bologna Process (BP), European governments discuss HE policy changes. It seems that the BP has become an important space for soft diplomacy with neighbouring countries where participants strive to overcome obstacles to create a European Higher Education Area (EHEA). Enhancing the quality of teaching is also a core mission of the BP. This study shows how the BP helps to build the trust necessary for cross-border academic cooperation, successful learning mobility, and the mutual recognition of study periods and qualifications earned abroad.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".