Early childhood education and care (ECEC) and reading acquisition in at-risk readers: Does quantity matter?
Bibliographic record
Abstract
Early childhood education and care (ECEC), involving nonparental preschool supervision and programming for children in centre-based and school contexts, can influence academic outcomes during elementary years. We present preliminary findings identifying quantity and timing as important but relatively little investigated aspects of ECEC experience. We focus on associations among cumulative ECEC hours during the early preschool (birth to 24 months prior to the commencement of Grade 1) and late preschool (24 months prior to Grade 1) periods, and later growth in children’s achievement in letter naming, decoding, and reading comprehension. Ninety-four children, aged 72 to 91 months at the outset, were examined across five testing occasions spanning the first two-and-a-half years of formal schooling. The quantity of ECEC in the early and late periods did not independently account for significant variance in initial status or growth in reading outcomes. However, differential influences of timing and quantity in children at risk of developing reading difficulty and in children experiencing family SES risk were found for decoding and comprehension. Results are consistent with a contextual support model of influence of ECEC quantity, and they indicate contextual circumstances in which ECEC experience may be a protective factor for early reading development.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".