From Enforced Closure to Regulated Mobility: The Need for a Paradigm Shift in Migration Policies
Bibliographic record
Abstract
Editor’s note: the following text is an edited version of the keynote address delivered on May 13, 2015, at the 8th Annual Conference of the Canadian Association for Refugee and Forced Migration Studies (CARFMS) at Ryerson University, Toronto. Ladies and gentlemen, I would like to thank the organisers, and in particular my colleague and friend Prof. Idil Atak, for inviting me to this exchange with you. It is a rare occasion and I’m very grateful for the opportunity. I was asked to share with you a number of ideas coming from my experience as UN Special Rapporteur on the human rights of migrants, especially on the relationship between criminalisation, precariousness, and human rights protection. The thoughts I’m sharing with you are mostly based on my knowledge of international human rights and refugee law, my country visits – Albania, Tunisia, Turkey, Italy, Greece, the European Union (Brussels), Qatar, Nepal, Sri Lanka, and Malta – and my various thematic reports on the detention of migrants, climate change and migration, the management of the external borders of the European Union, Global Migration Governance, the labour exploitation of migrants, and the human rights of migrants in the post-2015 sustainable development agenda. They are also inspired by the most recent policy announcements made by the European Union, including the European Migration Agenda announced today in Brussels.
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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 | 0.026 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.023 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".