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Record W2809958358 · doi:10.1177/1468798418783326

The role of digital technology in teen mothers’ and their children’s literacy

2018· article· en· W2809958358 on OpenAlexaff
Sharon Murphy, Marva Headley

Bibliographic record

VenueJournal of Early Childhood Literacy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsYork University
Fundersnot available
KeywordsLiteracyDigital literacyDevelopmental psychologyPsychologyEmergent literacyPedagogy

Abstract

fetched live from OpenAlex

Teen mothers and their children are often seen as being involved in a cycle of low literacy. However, as people whose lives have paralleled societal changes in literacy and digital technology, there is a possibility that the literacy experiences of today’s teen mothers and their children differ from those of the past. This study explores the role of digital technology in the literacy lives of teen mothers and their children. Eight teen mothers were interviewed about their recollections of their early childhoods as well as their contemporary experiences with digital technology in their own lives and in the lives of their children. The early experiences of teen mothers were both educational and entertainment oriented, whereas their later experiences are quite varied. However, the experiences that their children have with digital technology and literacy reflect a prioritization of educational uses as well as a managed approach to their children’s engagements that also emphasizes the children’s interests and development.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.243
Teacher spread0.239 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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