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Record W2907897589

Examining the Quality of Storybook Reading Sessions Between Parents and Children

2018· dissertation· en· W2907897589 on OpenAlexaboutno aff
Shaneha Patel

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2018
Typedissertation
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)PsychologyDevelopmental psychologyExtant taxonLiteracySession (web analytics)Shared readingQuality (philosophy)Emergent literacyPedagogyComputer science
DOInot available

Abstract

fetched live from OpenAlex

The home environment is an important contributor to children’s literacy experiences. One activity that is frequently investigated is parent-child storybook reading. However, despite the extant research, the quality of these book-reading interactions has often been overlooked. Therefore, the present study examined the quality of book-reading sessions between 60 parent-child dyads recruited from local schools in Montréal, Canada. Storybook reading sessions were recorded and behaviours were coded for types of talk (immediate, non-immediate, illustration production, and print referencing) and engagement (behaviours showing enjoyment in the reading session). The results of hierarchical multiple regressions demonstrated that parents’ non-immediate talk and engagement accounted for unique variance in children’s non-immediate talk and engagement, above and beyond children’s own behaviours. Parallel regressions demonstrated that children’s non-immediate talk and engagement accounted for unique variance in parents’ non-immediate talk and engagement, above and beyond parents’ own behaviours. These results emphasize the reciprocal role that both parents and children play during storybook reading activities. Implications for parents’ practises are discussed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.369
Teacher spread0.295 · 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 designObservational
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

Citations0
Published2018
Admission routes1
Has abstractyes

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