Introduction. Between Protection and Harm. Negotiated Vulnerabilities in Asylum Laws and Bureaucracies
Bibliographic record
Abstract
Abstract This book started from a common observation: ‘Vulnerability’ is increasingly playing a role in institutional discourses and practices, when developing and implementing policies and measures towards migrants seeking protection (such as refugees and asylum seekers)—and this in a wide array of contexts, which range from organizing asylum processes in countries in the global north and evaluating asylum claims, to selecting refugees for resettlement, and to developing and implementing aid programmes for refugees in first countries of asylum that are also countries in the global south. ‘Vulnerability’ can be a criterion that asylum seekers and refugees should meet in view of accessing certain advantages—such as resettlement, specific services in reception facilities and camps (specialised healthcare, housing, etc), or procedural accommodations as part of the asylum process (additional support and delays in preparing the asylum interview, interview by a specially trained public servant, etc) (UNHCR, 2011; Dir 2013/33/EU; Dir 2013/32/EU). ‘Vulnerability’ can also be an overall consideration, to be integrated in transversal ways while designing asylum and migration policies, as well as the norms and guidelines that accompany their operationalisation (UNGA Res 73/195, Objective 7; Council of Europe, 2021). Yet, while attention to the vulnerabilities of migrants seeking protection reflects humanitarian concerns, its concrete effects still need to be considered from a critical perspective. As it plays an increasingly key role in the legal and bureaucratic processes that seek to identify migrants eligible for protection (such as the refugee status) and/or protection services (such as access to housing, food, healthcare, etc), ‘vulnerability’ turns into a selection-tool with implied exclusionary effects that may also cause harms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".