A Longitudinal Follow-up Study of the Doodle Den After-school Programme Childhood Development Initiative
Bibliographic record
Abstract
More than a quarter of children in Ireland are living in, or are at risk of, poverty. Children who grow up in poverty are more likely to leave school early and without having attained the fundamental literacy skills. Literacy is widely acknowledged as the foundation for academic attainment across the curriculum. Children who fall behind in literacy at an early stage are likely to remain behind (Brooks, 2007; Francis et al, 1996; Juel, 1988), with consequences for later academic achievement and access to employment.The importance placed on the development of children's literacy has resulted in the design of numerous interventions for children in the form of programmes, products, practices and policies. Whilst many of these initiatives take place within normal school hours, after-school programmes are increasingly being adapted from their traditional role, which focused on childcare and recreational activities, to one focused on academic achievement. Some interventions have been shown to improve literacy outcomes in the short term; however, questions remain about whether these improvements are sustained over the longer term. With this in mind, the current report outlines the results of a follow-up to a randomised controlled trial evaluation of an after-school literacy programme (Doodle Den).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".