PUB TYPE Reports Evaluative/Feasibility (142) Dissertations /Theses Masters Theses (042) Tests /Evaluation Instruments (160)
Bibliographic record
Abstract
A study examined whether reading aloud to young children would have an effect on their reading success in first grade. Subjects, 45 first-grade students in Wood-Ridge, New Jersey, were given a questionnaire to be completed by their parents. The three first-grade teachers provided students reading aptitude scores, based on teacher observation and test scores from the MacMillan/McGraw-Hill "A i'ew View " reading series. The questionnaires were returned on a voluntary basis with a response rate of 84.447.. Students were divided into two first-grade based upon whether they were rich or poor in their literacy experiences. A t-test was used to analyze the differences, if any, between the reading questionnaire/achievement of the samples. Results indicated that there was almost a 24-point difference between the mean grade achievement of the samples at the end of the second quarter and this difference was highly significant. There was no strong evidence however to support that reading to children at a young age would better help them succeed in first grade. (Contains 2 tables of data; related research, 25 references, and a sample letter and questionnaire are appended.) (CR) * Reproductions supplied by EDRS are the best that can be made from the original document.
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 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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.695 | 0.474 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".