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Record W4385755641 · doi:10.1177/00220027231195066

Changes in Perceptions of Border Security Influence Desired Levels of Immigration

2023· article· en· W4385755641 on OpenAlexafffund
Ryan C. Briggs, Omer Solodoch

Bibliographic record

VenueJournal of Conflict Resolution · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Guelph
FundersConcordia UniversityUniversity of GuelphUniversity of Pennsylvania
KeywordsImmigrationBorder SecurityPerceptionIsolation (microbiology)Immigration policyGovernment (linguistics)Control (management)Demographic economicsPolitical scienceEconomicsPsychologyPublic administrationLaw

Abstract

fetched live from OpenAlex

Security concerns about immigration are on the rise. Many countries respond by fortifying their borders. Yet little is known about the influence of border security measures on perceived threat from immigration. Borders might facilitate group identities and spread fear of outsiders. In contrast, they might enhance citizens’ sense of security and control over immigration. We test these claims using survey experiments run on a quota sample of over 1000 Americans. The findings show that allocating more government resources to border security increases desired levels of immigration. This effect is likely driven by a sense of control over immigration, induced by border security measures even when the number or characteristics of immigrants remain unchanged. Our findings suggest that border controls, which are widely considered as symbols of closure and isolation, can increase public support for immigration.

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)0.009
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.038
GPT teacher head0.360
Teacher spread0.323 · 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
Published2023
Admission routes2
Has abstractyes

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