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Record W2609827287 · doi:10.3138/cmlr.3334

Immigrant Adolescents Investing in Korean Heritage Language: Exploring Motivation, Identities, and Capital

2017· article· en· W2609827287 on OpenAlexvenueno aff
Jung-In Kim

Bibliographic record

VenueCanadian Modern Language Review/ La Revue canadienne des langues vivantes · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationHeritage languageCompetence (human resources)PsychologySocial capitalSocial psychologyDevelopmental psychologySociologyPedagogySocial sciencePolitical science

Abstract

fetched live from OpenAlex

The current study examined the perspectives of seven immigrant adolescents on aspects of their lives that informed their determined and autonomous motivations to learn Korean as a heritage language (HL) in the United States. Constant comparative analyses of interview data showed that, although all of the students experienced determined motivations in their lived experience, their motivational experiences to learn Korean varied across contexts (e.g., home, American school, and Korean HL school) and showed at least three meaningfully distinct patterns. The students’ determined motivations to learn Korean were informed by their negotiated HL learner identities within their immediate and imagined communities. The various forms of social, cultural, and symbolic capital within those communities seemed to fulfill the students’ psychological needs, such as relatedness and competence, allowing them to experience determined motivations.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.338
Teacher spread0.272 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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