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Record W2599878592 · doi:10.3138/jcfs.45.4.517

Language and Non-linguistic Brokering: Diversity of Experiences of Immigrant Young Adults from Eastern Europe

2014· article· en· W2599878592 on OpenAlexvenueno aff
Vanja Lazarevic, Marcela Raffaelli, Angela Wiley

Bibliographic record

VenueJournal of Comparative Family Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationDiversity (politics)FeelingCitizenshipCultural diversitySociologyFocus (optics)PsychologyGender studiesSocial psychologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Brokering, or the work immigrant youth do to help their families adapt to a new society, is a common phenomenon in immigrant families, but has only recently been explored in research studies. Most researchers focus on language brokering (translating and interpreting) rather than non-linguistic types of brokering (e.g., helping parents study for the citizenship exam), and studies have mainly involved Latino adolescents. The current study simultaneously examines different types of, and feelings about, brokering work among first-generation immigrant young adults (M age = 22.92, SD = 2.89; 63.5% female) from Eastern Europe (N = 197). Both existing and newly developed brokering measures were administered to the participants. Findings indicate that immigrant youth engage in non-linguistic brokering for their parents more often than in language brokering, and feel more positive about non-linguistic brokering than language-focused brokering. Further, findings point to the diversity of immigrant experiences, and the implications of these findings are discussed in detail.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.042
GPT teacher head0.330
Teacher spread0.288 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2014
Admission routes1
Has abstractyes

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