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Record W2424056678 · doi:10.1177/1468798416653175

‘i’Babies: Infants’ and toddlers’ emergent language and literacy in a digital culture of iDevices

2016· article· en· W2424056678 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Early Childhood Literacy · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLiteracyPerspective (graphical)Digital literacyLanguage acquisitionPsychologyDevelopmental psychologyFamily literacyPedagogySociologyMathematics educationComputer science

Abstract

fetched live from OpenAlex

Children today are growing up in a digital world that is changing and advancing at an unprecedented rate. While some adults may struggle to keep up with new technological gadgets, we find our very young may be quite at ease with the use of digital technologies, even before learning to speak. This study builds on a foundation of family literacy studies that looks at the literacies children are exposed to within their home environments. Given the influx of technology in children’s home environments, it is important to understand children’s digital literacy developments from a family literacy perspective. Studying two very young children and their families interacting with these new devices provides a deep and detailed look into how digital technologies might be influencing young children’s language and literacy development in first and second languages. Findings from this study can inform parents and educators of what, why and how young children interact and learn with digital devices.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.006
GPT teacher head0.271
Teacher spread0.264 · 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