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Record W3129368437 · doi:10.1093/isq/sqab005

Subgroup Differences in Implicit Associations and Explicit Attitudes during Wartime

2021· article· en· W3129368437 on OpenAlexaff
Aaron Erlich, Calvin Garner

Bibliographic record

VenueInternational Studies Quarterly · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsMcGill University
Fundersnot available
KeywordsEthnic groupAdversaryHostilitySocial psychologyIn-group favoritismPolitical sciencePopulationIdentity (music)Context (archaeology)PsychologyImplicit-association testTest (biology)SociologySocial identity theoryDemographySocial groupGeographyLawMathematics

Abstract

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Abstract When their country is at war, individuals express support for their government and hostility toward the foreign adversary, leading to the “rally ′round the flag” effect. What is less understood is how, during a rally, ethnic identity and proximity to conflict relate to attitudes toward the home state and the adversary. Moreover, individuals may feel pressure to answer patriotically when asked about the conflict, particularly individuals who share an ethnic identity with the majority population of the foreign adversary, leading to biased measures of opinion. We study these dynamics in the context of Ukraine's ongoing war with Russia, comparing responses from self-identified ethnic Ukrainians and Russians in four cities in Ukraine. Using a lab-based implicit association test (IAT) and survey with 600 respondents, we examine whether respondents’ implicit biases, reflexive preferences that are hard to manipulate, match their explicitly stated preferences for either Ukraine or Russia. We find that, on average, ethnic Ukrainians and Russians in Ukraine are explicitly and implicitly pro-Ukraine, although we observe slightly lower levels of pro-Ukraine bias among ethnic Russians. We also find that 70 percent of those who are implicitly pro-Russia are explicitly neutral or pro-Ukraine, highlighting the need to study implicit associations in sensitive settings. Cuando un país está en guerra, la población expresa su apoyo al gobierno y hostilidad hacia el adversario extranjero, lo cual produce el efecto de “agruparse alrededor de la la bandera” [“rally ‘round the flag”]. Lo que no se comprende bien es la manera en que, durante una agrupación alrededor de la bandera, la identidad étnica y la proximidad al conflicto se relacionan con ciertas actitudes hacia el estado local y el adversario. Además, las personas pueden sentirse presionadas para responder de forma patriótica cuando se les pregunta acerca del conflicto, en particular aquellas que comparten una identidad étnica con la mayoría de la población del adversario extranjero, algo que da lugar a medidas de opinión sesgadas. Estudiamos estas dinámicas en el contexto de la guerra actual entre Ucrania y Rusia, comparando respuestas de ucranianos y rusos autoidentificados étnicamente en cuatro ciudades de Ucrania. Utilizando una prueba de asociación implícita (implicit association test, IAT) de laboratorio y una encuesta con 600 participantes, examinamos si los sesgos implícitos (preferencias reflexivas que son difíciles de manipular) de los encuestados coinciden con sus preferencias expresadas, ya sea a favor de Ucrania o de Rusia. Descubrimos que, en promedio, las personas de origen étnico ucraniano y ruso en Ucrania están explícita e implícitamente a favor de Ucrania, aunque observamos niveles levemente más bajos de sesgo a favor de Ucrania en personas de origen étnico ruso. Además, observamos que el 70 percent de aquellas personas que están implícitamente a favor de Rusia están explícitamente a favor de Ucrania o tienen una postura neutral, lo cual resalta la necesidad de estudiar asociaciones implícitas en entornos sensibles. Lorsque leur pays est en guerre, les individus expriment leur soutien pour leur gouvernement et leur hostilité envers l'opposant étranger, ce qui mène à un effet de « ralliement autour du drapeau ». Cependant, ce qui est moins compris, c'est la mesure dans laquelle l'identité ethnique et la proximité du conflit ont un lien avec les attitudes envers l’État de résidence et l'opposant durant ce ralliement. De plus, les individus peuvent ressentir une pression les poussant à répondre patriotiquement lorsqu'ils sont interrogés sur le conflit, particulièrement les individus qui partagent l'identité ethnique de la majorité de la population de l'opposant étranger, ce qui mène à des mesures d'opinion biaisées. Nous étudions ces dynamiques dans le contexte du conflit continu entre l'Ukraine et la Russie en comparant les réponses d'individus qui s'identifient d'eux-mêmes ethniquement en tant que Russes ou qu'Ukrainiens dans quatre villes d'Ukraine. Nous utilisons un test d'association implicite mené en laboratoire et une enquête sur 600 participants pour examiner si les préjugés implicites des participants, des préférences réflexives difficiles à manipuler, correspondent à leurs préférences explicitement déclarées envers l'Ukraine ou la Russie. Nous avons constaté qu'en moyenne, les Ukrainiens et Russes ethniques vivant en Ukraine étaient explicitement et implicitement pro-Ukraine, bien que nous ayons observé des niveaux de préférences pro-Ukraine légèrement inférieurs chez les Russes ethniques. Nous avons également découvert que 70 percent des personnes qui étaient implicitement pro-Russie étaient explicitement neutres ou pro-Ukraine, ce qui met en évidence le besoin d’étudier les associations implicites dans les environnements sensibles.

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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)0.001
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.383
Teacher spread0.328 · 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".

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Citations18
Published2021
Admission routes1
Has abstractyes

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