Extreme hardship, care ethics, and humanitarian protection: Lessons from Libya and Italy
Bibliographic record
Abstract
This paper focuses on the extreme hardship suffered by migrants crossing through Libya to reach Italy and Europe.The paper documents and defines the notion of extreme hardship and argues in favour of an ethics of care that provides for protection for those migrants who may not be asylum seekers for what concerns their initial motivation for migrating but who need humanitarian protection because of the harm suffered while en route.Starting with a normative exploration of how an ethics of care can and should inform the policy of countries of arrival, this paper analyses the specific case of Italy and the emerging case law and legal practice in relation to the humanitarian stay permits.Based on the analysis of relevant scholarly literature, policy and legal texts and interviews with expert informants (lawyers and judges) and taking stock of an innovative practice that emerged in Italy in the period 2015-2020, the paper also discusses similar provisions in other European countries and argues for the possibility to develop and codify a humanitarian permit, at national or also European level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".