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What Motivates the Gatekeepers? Explaining Governing Party Preferences on Immigration

2008· article· en· W1979461809 on OpenAlexaff
Christian Breunig, Adam Luedtke

Bibliographic record

VenueGovernance · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImmigrationPoliticsScholarshipConstruct (python library)Immigration reformImmigration policyPolitical economyPolitical scienceDisciplinePositive economicsSociologyLawEconomics

Abstract

fetched live from OpenAlex

Most scholarship on immigration politics is made up of isolated case studies or cross‐disciplinary work that does not build on existing political science theory. This study attempts to remedy this shortcoming in three ways: (1) we derive theories from the growing body of immigration literature, to hypothesize about why political parties would be more or less open to immigration; (2) we link these theories to the broader political science literature on parties and institutions; and (3) we construct a data set on the determinants of immigration politics, covering 18 developed countries from 1987 to 1999. Our primary hypothesis is that political institutions shape immigration politics by facilitating or constraining majoritarian sentiment (which is generally opposed to liberalizing immigration). Our analysis finds that in political systems where majoritarianism is constrained by institutional “checks,” governing parties support immigration more strongly, even when controlling for a broad range of alternative explanations.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.032
GPT teacher head0.274
Teacher spread0.241 · 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 designQualitative
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

Citations59
Published2008
Admission routes1
Has abstractyes

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