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Record W2316936737 · doi:10.1111/cdep.12181

Introducing Remote Enculturation: Learning Your Heritage Culture From Afar

2016· article· en· W2316936737 on OpenAlexaff
Gail M. Ferguson, Catherine L. Costigan, Christy V. Clarke, Julianna Ge

Bibliographic record

VenueChild Development Perspectives · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEnculturationSocializationConstruct (python library)AcculturationPsychologyTransculturationEthnic groupSociologySocial psychologyAnthropologyPedagogy

Abstract

fetched live from OpenAlex

Abstract Can youth living outside their heritage country become enculturated from afar via avenues of modern globalization? In this article, we expand the theory of how heritage cultural socialization occurs in transnational families by introducing the construct of remote enculturation as a modern form of cultural transmission. Remote enculturation falls within the cultural socialization category of ethnic/racial socialization and is a form of enculturation that involves learning aspects of one's heritage culture via indirect or intermittent exposure, or both. We compare and contrast remote enculturation with traditional enculturation, proposing that self-initiated remote enculturation, in particular, has strong ties with the development of identity. Research on immigrants’ consumption of foreign media and on parenting international adoptees supports remote enculturation as a distinct avenue of cultural learning, as do the experiences of youth from immigrant families. We conclude with a research agenda to empirically evaluate the construct of remote enculturation.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.004
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.017
GPT teacher head0.301
Teacher spread0.283 · 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 designTheoretical or conceptual
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

Citations57
Published2016
Admission routes1
Has abstractyes

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