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Record W3094816759 · doi:10.1177/1948550620967230

Rising Ethnic Diversity in the United States Accompanies Shifts Toward an Individualistic Culture

2020· article· en· W3094816759 on OpenAlexafffund
Alex C. Huynh, Igor Grossmann

Bibliographic record

VenueSocial Psychological and Personality Science · 2020
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndividualismEthnic groupDiversity (politics)Cultural diversitySocial psychologySociocultural evolutionGlobePsychologySociologyGender studiesPolitical scienceAnthropologyLaw

Abstract

fetched live from OpenAlex

We investigate the relationship between ethnic diversity and the rise of individualism in the United States during the 20th and 21st centuries. Tests of the historical rates of ethnic diversity alongside individualistic relational structures (e.g., adults living alone, single-/multi-child families) from the years 1950 to 2018 reveal that societal and regional rates of ethnic diversity accompanied individualistic relational structures. These effects hold above and beyond time-series trends in each variable. Further evidence from experimental studies ( N = 707) suggests that the presence of, and contact with, ethnically diverse others contributes to greater individualistic values (e.g., the importance of uniqueness and personal achievement). Converging evidence across societal-, regional-, and individual-level analyses suggests a systematic link between ethnic diversity and individualism. We discuss the implications of these findings for sociocultural livelihood in light of the rising rates of ethnic diversity across the globe.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.533
GPT teacher head0.483
Teacher spread0.050 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

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