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Record W4404205648 · doi:10.1007/978-3-031-74866-0_2

Actors and Their Networks: Scope for Adaptation to and Contestation of Global Norms for Refugee Protection

2024· book-chapter· en· W4404205648 on OpenAlexaboutno aff
Leiza Brumat, Andrew Geddes, Andrea Pettrachin

Bibliographic record

VenueInternational perspectives on migration · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeScope (computer science)Adaptation (eye)Political scienceSociologyEnvironmental ethicsComputer sciencePsychologyLawPhilosophyNeuroscience

Abstract

fetched live from OpenAlex

Abstract This chapter analyses the extent of differential incorporation at domestic level of global norms and standards for asylum-seekers and refugees in Bangladesh, Brazil, Canada, Jordan, South Africa and Türkiye. The chapter identifies sources of variation and their effects on domestic level responses to global norms and standards associated with refugee protection. The Chapter identifies four potential domestic-level responses to global norms and standards: adoption, adaptation, resistance and rejection. It then provides empirical evidence combining network analysis supplemented by interviews with 99 elite actors in the six case countries with interviewees defined by their leadership role in relation to asylum and refugee governance. The Chapter identifies sources of variation in asylum/refugee governance and identifies: (i) variation in the meaning of protection; (ii) the important role played by IOs in mediating the relationship between the domestic and the international levels; (iii) scope for contestation of global norms; (iv) how contestation can lead both to watering-down of global norms and standards; and (v), in the case of Brazil, how protection standards can be upgraded at national level through use of regional norms and standards that are seen as more progressive than global norms and standards

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.309
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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