An Evaluation of a Dialogic Book-Reading Program for At Risk Children
Bibliographic record
Abstract
Children from low-income backgrounds are at a higher risk for reading difficulties partly because they are read to less frequently in the home (Adams, 1990). When shared reading does occur in low-income homes, it is usually of poorer quality when compared to reading in middle- or upper-income homes (Arnold, Lonigan, Whitehurst, Epstein, 1994). Dialogic reading, a form of enhanced discussion and structured questioning during shared-book reading, can be a cost effective way of improving the language and literacy skills of young children. The current research examines the effectiveness of a community-based, four-month dialogic reading intervention called the Dialogic Reading Club (pseudonym used to protect the identity of the program). Eighteen children aged 38 months to 68 months (M age = 58.22 months) that attended the intervention were compared with 18 children aged 39 months to 71 months (M age = 53.11 months) that did not attend the intervention on measures of expressive vocabulary, word reading, concepts of print, and narrative ability. Controlling for pre-test differences on the same post-test measures, ANCOVAs revealed significant differences in word reading, F (1,33) = 5.40,pF (1,33) = 9.28, pp = .09), produced more words (p = .08), and produced a greater diversity of words (p = .08). No differences were found on expressive vocabulary. The benefits of incorporating dialogic reading strategies in a short-term reading intervention for young children are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".