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Record W2890718766 · doi:10.1073/pnas.1804002115

Perspective taking can promote short-term inclusionary behavior toward Syrian refugees

2018· article· en· W2890718766 on OpenAlexfundno aff
Claire L. Adida, Adeline Lo, Melina Platas

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
FundersMcGill UniversityJohns Hopkins UniversityNational Science Foundation
KeywordsPerspective (graphical)RefugeeTest (biology)Inclusion (mineral)OutgroupPsychologySocial psychologyPolitical scienceDemographic economicsLawEconomics

Abstract

fetched live from OpenAlex

Social scientists have shown how easily individuals are moved to exclude outgroup members. Can we foster inclusion instead? This study leverages one of the most significant humanitarian crises of our time to test whether, and under what conditions, American citizens adopt more inclusionary behavior toward Syrian refugees. We conduct a nationally representative survey of over 5,000 American citizens in the weeks leading up to the 2016 presidential election and experimentally test whether a perspective-taking exercise increases inclusionary behavior in the form of an anonymous letter supportive of refugees to be sent to the 45th President of the United States. Our results indicate that the perspective-taking message increases the likelihood of writing such a positive letter by two to five percentage points. By contrast, an informational message had no significant effect on letter writing. The effect of the perspective-taking exercise occurs in the short run only, manifests as a behavioral rather than an attitudinal response, and is strongest among Democrats. However, this effect also appears in the subset of Republican respondents, suggesting that efforts to promote perspective taking may move to action a wide cross-section of individuals.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.663
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.079
GPT teacher head0.420
Teacher spread0.341 · 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.

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

Citations251
Published2018
Admission routes1
Has abstractyes

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