Refugees and Open Borders: How sustainable is the Schengen?
Bibliographic record
Abstract
The Schengen has been a prime example of European integration, providing citizens of member states the unique experience of travelling across borders without the inconvenience of border checks. However, the recent peak in the flow of refugees and a changing political environment has challenged the agreement and out the future of open borders in Europe into question. This article initially establishes the background on the Schengen and Dublin conventions, the benefits they have brought to member states and the role they have played in European integration. Subsequently, the challenges brought by refugees and the reactions of European nations is then discussed and finally the attempts by members to address the current challenges are assessed. In summary, I argue that the situation can only be resolved with political will from all member states in order to make the tough decisions required to maintain an achievement that was itself reached after a collective effort by all member states. There is also a need to realize the extra burden that frontier states are bearing and the need for sharing the responsibility of any collective decision. Inaction or counterproductive measures would either challenge the moral responsibility of the EU in protecting those risking their lives to reach its shores or result in the end of a borderless Schengen zone. Both scenarios are undesired, underscoring the importance of robust, collective action.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.008 | 0.006 |
| 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".