Best practices in after-school programing for secondary school students
Bibliographic record
Abstract
Over the past twenty years, After-School Programs (ASPs) in both Canada and the United States have increasingly become increasingly as a potential tool to create more equitable academic outcomes between different groups of students. Despite the prevalence of ASPs, program developers and school administrators know little about the program factors or components that produce desirable outcomes in their target populations. After reviewing 124 different sources, including 117 academic journal articles, six technical reports and one book, six best practices for ASPs for secondary school students were identified: clear mission; safe, positive, and healthy climate; recruitment of a diverse mix of youth; addresses barriers to participation; hiring, training, and retaining high quality staff; and use of a flexible curriculum with engaging content. Most of the research on best practices in ASPs focuses on structural elements, such as participant recruitment and human resources. This review also calls for program developers and school administrators to invest in more rigorous research and evaluation efforts to generate reliable knowledge and build program evaluation capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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 teacher head, 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".