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Record W4388001564 · doi:10.1093/ijpor/edad027

The Relationship Between University Education and Pro-Immigrant Attitudes Varies by Generation: Insights From Japan

2023· article· en· W4388001564 on OpenAlexaff
Gento Kato, Fan Lu

Bibliographic record

VenueInternational Journal of Public Opinion Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsQueen's University
FundersJapan Society for the Promotion of Science
KeywordsImmigrationHigher educationDemocracyPolitical sciencePublic opinionFirst generationDemographic economicsUniversity educationEconomic growthDevelopment economicsSociologyDemographyEconomicsLawPopulation

Abstract

fetched live from OpenAlex

Abstract While there is lively debate on whether higher education cultivates support for immigrants in North America and Western Europe, there is little discussion on the extent to which the relationship generalizes beyond these continents. In light of Japan’s growing reliance on foreign workers, increase in university enrollment rates, as well as efforts to internationalize universities over the last half-century, we explore the relationship between university education and Japanese attitudes toward immigrants. Using two surveys asking an overlapping set of questions in 2009 and 2022, we find the relationship between university education and pro-immigrant attitudes varies by generation. Otherwise positive connections are significantly weakened for Japanese who entered universities in the 1990s through 2000s. Even though Japan is a modern democracy with well-developed higher education institutions, these institutions do not always correlate with more supportive attitudes toward immigrants. Our findings underscore the dynamic nature of higher education’s role in shaping public opinion outside of North America and Western Europe.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.209
GPT teacher head0.444
Teacher spread0.235 · 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 designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

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