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Record W2607341911 · doi:10.20360/g22p5w

To Digital or not to Digital: How Mothers are Navigating the Digital World with their Young Children

2017· article· en· W2607341911 on OpenAlexaffvenue
Laura Teichert

Bibliographic record

VenueLanguage and Literacy · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDigital literacyDigital mediaLiteracyPsychologyDevelopmental psychologyPedagogyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

We know many children are using digital tools, such as mobile phones and tablets. Much has been debated about the appropriateness of these tools in the lives of young children (e.g., American Academy of Pediatrics, 2001, 2011; National Association for the Education of Young Children, 2012). Yet, parents are engaged in digital practices. Adults’ beliefs about the appropriateness of digital media for children influence the environments they create for young children and potentially influence children’s exposure to and interactions with digital tools. This paper describes the digital literacy practices of three families and reports on the tensions mothers felt in whether to allow their children to participate in these practices.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0060.006
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.285
Teacher spread0.274 · 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 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

Citations23
Published2017
Admission routes2
Has abstractyes

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