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Record W2982616288 · doi:10.1007/s12134-019-00738-0

Factoring in Societal Culture in Policy Transfer Design: the Proliferation of Private Sponsorship of Refugees

2019· article· en· W2982616288 on OpenAlexaboutno aff
Daniel Bertram, Ammar Maleki, Niels Karsten

Bibliographic record

VenueJournal of International Migration and Integration / Revue de l integration et de la migration internationale · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsnot available
FundersUniversiteit van Tilburg
KeywordsFactoringConverseRefugeePolicy transferPerspective (graphical)Political sciencePublic relationsSociologyBusinessComputer scienceLawPublic administrationEpistemology

Abstract

fetched live from OpenAlex

Abstract The Canadian model of private sponsorship schemes (PSS) for refugees is becoming an increasingly popular target for policy transfer in the field of migration. This article argues that the influence of societal culture on this transplanting process has played an underexplored role in the literature. We seek to provide original guidance for factoring in cultural elements into the policy transfer framework by demonstrating how specific design choices in PSS transfer display clear cultural associations. A tentative study of nine countries that have adopted different models of PSS corroborates this hypothesis empirically. Our preliminary findings suggest that cultural compatibility may indeed increase the effectiveness of a policy transfer in some instances, while culturally preferred choices being adopted in other cases may result in suboptimal design. This converse interplay indicates that cultural awareness constitutes a crucial element of successful transfer processes and stresses the need to adopt a culturally sensitive perspective more frequently and more explicitly.

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)0.047
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.060
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.007
Scholarly communication0.0070.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.027
GPT teacher head0.353
Teacher spread0.326 · 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 designQualitative
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

Citations15
Published2019
Admission routes1
Has abstractyes

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Same venueJournal of International Migration and Integration / Revue de l integration et de la migration internationaleSame topicPolicy Transfer and LearningFrench-language works237,207