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Record W4385986340 · doi:10.1080/1369183x.2023.2245149

Humanitarian bargains: private refugee sponsorship and the limits of humanitarian reason

2023· article· en· W4385986340 on OpenAlexafffundabout
Anna C. Korteweg, Shauna Labman, Audrey Macklin

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of WinnipegUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoPierre Elliott Trudeau Foundation
KeywordsRefugeeGeneral partnershipPolitical scienceHumanitarian aidState (computer science)Syrian refugeesPublic relationsPublic administrationLaw

Abstract

fetched live from OpenAlex

This article analyzes Canada's private sponsorship of refugees to explore the conceptual and practical limits of humanitarian reason. In private refugee sponsorship, sponsors channel their humanitarian impulse to resettle refugees in a time-limited partnership with both the state and the refugees they sponsor. To gain insight into how sponsors perform their role in this structurally and temporally bounded trajectory, we conducted a national online survey of 530 sponsors who volunteered to support Syrians resettled to Canada after November 2015. Our analysis draws primarily from written comments shared by survey respondents. We find that over time, sponsors resort to tacit ‘humanitarian bargains’ to mediate between their initial commitment to save refugees’ lives and their ongoing quotidian experiences of intervening in and shaping refugee lives. These bargains become visible when sponsors evaluate sponsorship by reference to a set of expectations and judgements about sponsored refugees, their fellow sponsors and the state. We suggest that the concept of the humanitarian bargain has explanatory force beyond refugee sponsorship.

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)0.004
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.124
GPT teacher head0.395
Teacher spread0.271 · 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

Citations11
Published2023
Admission routes3
Has abstractyes

Explore more

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