MétaCan
Menu
Back to cohort
Record W2494594397 · doi:10.1075/tilar.13.04pie

Language input and language learning

2014· book-chapter· en· W2494594397 on OpenAlexaff
Lara J. Pierce, Fred Genesee

Bibliographic record

VenueTrends in language acquisition research · 2014
Typebook-chapter
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsPerspective (graphical)PsychologyAffect (linguistics)Language acquisitionLinguisticsCognitive psychologyDevelopmental psychologyJoint attentionLanguage developmentCommunicationComputer scienceArtificial intelligenceMathematics educationAutism

Abstract

fetched live from OpenAlex

In this chapter we discuss how input during joint attention (JA) interactions between parents and children may vary in ways pertinent to language development in simultaneous bilinguals. In particular we discuss mother-father differences, exceptionally relevant in bilingual families where one parent may be the primary source of input for a given language. We illustrate the need to examine this interaction by presenting research on internationally-adopted (IA) children. While clearly not simultaneous bilinguals, IA children are relevant insofar as these children, like bilinguals, have reduced exposure to their new language. This, in turn, might affect the way parents verbally interact with their children, highlighting the importance of examining the role of input in simultaneous bilingual acquisition from an interactional perspective.

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)0.000
metaresearch head score (Gemma)0.000
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: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.003

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 head0.041
GPT teacher head0.393
Teacher spread0.353 · 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
GenreOther

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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueTrends in language acquisition researchSame topicLanguage Development and DisordersFrench-language works237,207