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Record W4252893629 · doi:10.31234/osf.io/aph8g

The Missing Side of Acculturation: How Majority-Group Members Relate to Immigrant and Minority-Group Cultures

2021· preprint· en· W4252893629 on OpenAlexaff
Jonas R. Kunst, Katharina Lefringhausen, David L. Sam, John W. Berry, John F. Dovidio

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsAcculturationEthnic groupImmigrationSalientMinority groupDemographicsSocial psychologyGroup (periodic table)PsychologySociologyPolitical scienceDemographyAnthropologyLaw

Abstract

fetched live from OpenAlex

In many countries, individuals who have represented the majority group historically are decreasing in relative size and/or perceiving that they have diminished status and power compared to those identifying as immigrants or members of ethnic minority groups. These developments raise several salient and timely issues including: (a) how majority-group members’ cultural orientations change as a consequence of increasing intercultural contact due to shifting demographics; (b) what individual, group, cultural and socio-structural processes shape these changes; and (c) the implications of majority-group members’ acculturation. Although research across several decades has examined the acculturation of individuals identifying as minority-group members, much less is known about how majority-group members acculturate in increasingly diverse societies. We present an overview of the state of the art in the emerging field of majority-group acculturation, identify what is known and needs to be known, and introduce a conceptual model guiding future research.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.036
GPT teacher head0.354
Teacher spread0.318 · 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 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

Citations6
Published2021
Admission routes1
Has abstractyes

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Same topicRacial and Ethnic Identity ResearchFrench-language works237,207