FACING THE DIFFICULT MANAGEMENT OF FLOWS \nOF MIGRANTS AND ASYLUM SEEKERS TO THE EU: \nITALY AS A CASE STUDY
Bibliographic record
Abstract
"Especially since mid-2015, Canadians have been touched and troubled by events taking place faraway, across the Atlantic, in Europe and the Mediterranean Basin, where scenes of migrants desperate for a chance to live in dignity and refugees fleeing for their lives have become commonplace. Unprepared for a surge of migrant flows at a magnitude unseen since World War II, European countries, individually as well as collectively, have been at a loss. An integrated European Union, the product of a peace, prosperity and stability project devised over sixty years ago, may be at risk. Inclusive to its member States, the socio-political effects of the open-border regime are looming larger and gloomier than the economic dreaded impact of a Grexit from the monetary union. In her piece “Facing the Difficult Management of Flows of Migrants and Asylum Seekers to the EU: Italy as a Case Study”, the Italian law professor and lawyer, Chiara Favilli, lays out the complexities of the European regional (international) refugee law. These embody an entanglement of EU, European human rights, member States’, and national non-member States’, laws. The challenge faced by Italy — a country gate to the EU — serves as a compelling case study. Favilli’s detailed account and analysis offer insights, and consequently raise issues, pertinent to international legal challenges faced also by Canada as it struggles with sharing in the human responsibility for the same refugees".
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".