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Record W4403184588 · doi:10.1007/978-3-031-69808-8_6

Selecting Refugees for Resettlement to Norway and Canada: Vulnerability, Integration and Discretion

2024· book-chapter· en· W4403184588 on OpenAlexaffabout
Erlend Paasche, Dagmar Soennecken, Ritika Tanotra

Bibliographic record

VenueIMISCOE research series · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsYork University
Fundersnot available
KeywordsRefugeeDiscretionVulnerability (computing)Political scienceGeographyComputer securityComputer scienceLaw

Abstract

fetched live from OpenAlex

Abstract This chapter examines how the concept of vulnerability is “translated” from legal bureaucratic discourses into actual policy and practice in the refugee resettlement context. In particular, we trace how the integration potential of refugees continues to be weighed against their vulnerabilities in the process. While resettlement is a voluntary commitment and not legally binding, states that have signed the 1951 Geneva Convention have agreed to share the responsibility of providing protection and solutions for refugees who cannot return to their country of origin. Through a comparative discussion of refugee resettlement in Canada and Norway, we shed light on some mechanisms through which the humanitarian focus on prioritizing the most vulnerable comes under pressure from competing political considerations and rationales. By examining instances of what we call the political or ‘tactical’ uses of resettlement, we aim not only to highlight its partisan and domestic political dynamics but also to open up questions of who is ultimately left behind and considered ‘too vulnerable’ for resettlement.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.101
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0110.007
Scholarly communication0.0070.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.057
GPT teacher head0.402
Teacher spread0.345 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueIMISCOE research seriesSame topicMigration, Refugees, and IntegrationFrench-language works237,207