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Record W2119372576 · doi:10.3102/0034654313483906

The Effects of Summer Reading on Low-Income Children’s Literacy Achievement From Kindergarten to Grade 8

2013· article· en· W2119372576 on OpenAlexaboutno aff
James S. Kim, David M. Quinn

Bibliographic record

VenueReview of Educational Research · 2013
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionReading (process)PsychologyLiteracyReading comprehensionLow incomeDevelopmental psychologyPedagogyPolitical scienceSocioeconomics

Abstract

fetched live from OpenAlex

This meta-analysis reviewed research on summer reading interventions conducted in the United States and Canada from 1998 to 2011. The synthesis included 41 classroom- and home-based summer reading interventions involving children from kindergarten to Grade 8. Compared to control group children, children who participated in classroom interventions, involving teacher-directed literacy lessons, or home interventions, involving child-initiated book reading activities, enjoyed significant improvement on multiple reading outcomes. The magnitude of the treatment effect was positive for summer reading interventions that employed research-based reading instruction and included a majority of low-income children. Sensitivity analyses based on within-study comparisons indicated that summer reading interventions had significantly larger benefits for children from low-income backgrounds than for children from a mix of income backgrounds. The findings highlight the potentially positive impact of classroom- and home-based summer reading interventions on the reading comprehension ability of low-income children.

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)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.011
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.040
GPT teacher head0.435
Teacher spread0.395 · 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 designObservational
Domainnot available
GenreReview

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

Citations209
Published2013
Admission routes1
Has abstractyes

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