The Changing Nature of Migration in the European Union: Social Identity and Cohesion in a Globalized World
Bibliographic record
Abstract
As countries such as the United States look to address questions of ethnic identity in the face of the changing nature of borders and migration, the states of the European Union are facing even greater change. With the expansion east and the free movement of people throughout the Union, the EU is now faced with a unique and challenging problem. Mass migration of East Europeans into Western Europe is changing the receiving state’s society and can potentially lead to conflict in the future. One needs only look at the changing face of Dublin in the wake of large-scale migration of Poles and other East Europeans to see how a society must quickly adapt to a new understanding of citizenship and identity. Moreover, as these migrants are arriving from European Union member states, they have established EU citizenship rights which limit how countries such as Ireland can control their own immigration policies. This paper will examine how this change in the nature of migration is affecting not only the receiving states of the EU but also the states from where the migrants came and the EU generally. This analysis, relying on both primary interviews and secondary sources, will shine a light on the concept of social cohesion in a globalized world and will provide a means of comparison for other states attempting to cope with similar issues of identity and transnational borders.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".