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Record W2472405168 · doi:10.1177/1367006916654351

Introduction to the special issue – Heritage language studies and early child bilingualism research: Understanding the connection

2016· article· en· W2472405168 on OpenAlexaboutno aff
Suzanne Aalberse, Aafke Hulk

Bibliographic record

VenueInternational Journal of Bilingualism · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsNeuroscience of multilingualismConnection (principal bundle)Heritage languageLinguisticsPsychologySociologyPhilosophy

Abstract

fetched live from OpenAlex

This issue is the result of a colloquium with the same title organized at the ISB 10 conference at Rutgers, USA, in May 2015. All the presenters, the discussant and one additional colleague have contributed to this collection of articles, which brings together linguists from the domain of heritage studies with those working on early child bilingualism (ECB). A language qualifies as a heritage language (HL) if ‘it is a language spoken at home or otherwise readily available to young children, and crucially this language is not the dominant language of the larger (national) society’ (Rothman, 2009, p. 156). HLs are learned early in life, either simultaneously with the dominant language or prior to the acquisition of the dominant language of the country; heritage speakers (HSs) are thus early bilinguals. There is, however, a gap between linguists studying ECB and linguists studying HSs. Linguists working on ECB mainly look at the early development of both languages in children growing up bilingually from birth or shortly afterwards and tend to report on similar developmental patterns in monolingual and bilingual children, and on (temporary) delay, acceleration or cross-linguistic influence in this development, mostly in Western Europe and Canada (De Houwer, 1990; Meisel, 1990; Paradis & Genesee, 1996). Linguists studying HSs, on the other hand, mainly look at young adults’ competence in their HL and tend to focus on signs of incomplete acquisition, mostly in the USA (Benmamoun, Montrul, & Polinsky 2013; Montrul, 2008; Polinsky, 2006). This special issue brings together linguists from both fields to find out how to make the connection: to what extent can we observe similarities in the reported results on child bilinguals and HSs and to what extent do we observe differences? What motivates these similarities and differences? The contributions in this special issue all shed light on the comparison and thereby create new questions. We will briefly discuss some general factors that may influence different outcomes of early bilingualism over the lifespan (see also Hulk & Marinis, 2011).

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)0.005
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.170
GPT teacher head0.506
Teacher spread0.337 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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