Seeking protection abroad. An overview of refugee protection regimes and current development
Bibliographic record
Abstract
Although migration has been an intrinsic part of humanity’s journey since its earliest stages (Crépeau, 2018) – both as a formative factor of the nation-state (Bloemraad, 2012) and as a catalyst for national and international tensions \n(Koslowski, 2002; Adamson and Tsourapas, 2019) – in the recent years, the issue of migration and refugee influxes has emerged as a major subject of public discourse in Western countries, including and particularly in the U.S., U.K., Austria, Germany, Italy, and France (Maurer et al., 2021; Shabi, 2019; Sevastopulo, 2018; Mayda, Peri, and Steingress, 2018; Otto and Steihardt, 2017). The political and social effects of the 2008 financial crisis, that is, a renewed political and ideological polarization as well as the rise of populist movements, have resulted in an \nincreased emphasis on the issue of migration (Makunda, 2018). \nVarious initiatives and policies have been put in place to either facilitate migration or to erect new barriers. In this chapter, we examine international and national frameworks for refugee protection and focus on current developments marked by the erection of walls, deportations and refoulement. Finally, we outline the context of the Canadian refugee protection system, which will serve as an \nintroduction to the subsequent chapters in this volume.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".