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Record W4392145539 · doi:10.3386/w32139

Out-group Penalties in Refugee Assistance: A Survey Experiment

2024· report· en· W4392145539 on OpenAlexafffund
Cristina Cattaneo, Daniela Grieco, Nicola Lacetera, Mario Macis

Bibliographic record

VenueNational Bureau of Economic Research · 2024
Typereport
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Toronto
FundersUniversità degli Studi di GenovaBanca d'ItaliaFondazione CariploUniversity of TorontoJohns Hopkins University
KeywordsRefugeeGroup (periodic table)PsychologyDemographic economicsPolitical scienceEconomicsChemistryLaw

Abstract

fetched live from OpenAlex

We study out-group biases in attitudes toward refugees, and the effect of European Union (EU) immigration policies on these views, using an online survey experiment including 4,087 Italian participants.We assess attitudes using donations to a randomly assigned group: Italian victims of violence or refugees fleeing wars in Ukraine or African countries.We also employ a novel measure, the share donated in cash.While donations indicated less support for African and Ukrainian refugees compared to Italian victims, the cash measure revealed a stronger prejudice against distant out-groups, with participants giving African refugees a smaller proportion of cash donations.This result was mainly driven by individuals with right-leaning political views.Providing information about immigration policy reforms that give the EU a more substantial role in receiving and allocating refugees had no impact.Textual analysis supports these findings.

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)0.012
metaresearch head score (Gemma)0.022
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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.438
GPT teacher head0.577
Teacher spread0.139 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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