MétaCan
Menu
Back to cohort
Record W3211190947 · doi:10.1017/xps.2021.26

Discriminatory Immigration Bans Elicit Anti-Americanism in Targeted Communities: Evidence from Nigerian Expatriates

2021· article· en· W3211190947 on OpenAlexaff
Aaron Erlich, Thomas Soehl, Annie Y. Chen

Bibliographic record

VenueJournal of Experimental Political Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsMcGill University
Fundersnot available
KeywordsImmigrationPublic opinionPolitical scienceRepresentation (politics)Intervention (counseling)Foreign policyImmigration policyPublic policyDevelopment economicsDemographic economicsPsychologyEconomicsLawPolitics

Abstract

fetched live from OpenAlex

Abstract Do discriminatory US immigration policies affect foreign public opinion about Americans? When examining negative reactions to US actions perceived as bullying on the world stage, existing research has focused either on US policies that involve direct foreign military intervention or seek to influence foreign countries’ domestic economic policy or policies advocating minority representation. We argue that US immigration policies – especially when they are perceived as discriminatory – can similarly generate anti-American sentiment. We use a conjoint experiment embedded in a unique survey of Nigerian expatriates in Ghana. Comparing respondents before and after President Trump surpisingly announced a ban on Nigerian immigration to the United States, we find a large drop (13 percentage points) in Nigerian’s favorability towards Americans.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.367
Teacher spread0.330 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Experimental Political ScienceSame topicTerrorism, Counterterrorism, and Political ViolenceFrench-language works237,207