PROTOCOL: Impacts of after‐school programs on student outcomes
Bibliographic record
Abstract
II. Background for the ReviewNationwide, an estimated 8 million children between the ages of 5 and 14 are frequently unsupervised after school (NIOST,2003).Recent data reveals that more than two-thirds of low-and moderate-income youth do not have parental supervision available after-school due to parental work requirements (Long & Clark, 1998; U.S. Bureau of Labor Statistics, 2000).These statistics are not surprising given the need for low-income families to meet pubic assistance work requirements, and the correlation between low to moderate income with single parent households.Research has linked such unsupervised time with increased risk-taking behaviors, victimization, and poorer academic outcomes (Dwyer et al., 1990; Newman et al., 2000; Osofsky, 1999; Posner & Vandell, 1999; Richardson et al., 1989; U.S. DHHS, 1995; U.S. DOE & U.S. DOJ, 2000).Unstructured, unsupervised after-school time has increasingly been seen by policy makers and the public as holding "risk and opportunity" (Hofferth, 1995).And, after-school programs have been touted as a means to reduce negative behaviors and improve positive outcomes, especially for lower-income, urban students.Within the last few years, after-school programming has seen tremendous growth.The federal government, states, localities and private foundations have invested substantial money and resources in programs.For example, appropriations for 21 st Century Community Learning Centers have increased from $40 million in 1998 to the near $1 billion that is currently appropriated for the program.In this short period of time, the number and strength of advocacy groups in this field has also experienced a great deal of growth.As evidence of their voices, tremendous fervor surrounded the recent release of the first year findings from the national evaluation of 21 st Community Learning Centers (CCLCs) (U.S. DOE, 2003).Several criticisms were directed at this report 2 , but arguably the strong responses to the report's primarily null findings were likely more reactions to the use of a single, high profile experimental study to recommend a 40% reduction in 21 st CCLC appropriations.The resulting lobbying and grass roots efforts to maintain or increase the 21 st CCLC appropriations served to highlight that continued support for a high investment in and expansion of after-school programming is not supported by a large or strong research base.Several quasi-experimental and non-experimental studies are frequently cited as evidence that after-school programming promotes positive developmental and emotional outcomes in low-income youth, may help to improve academic outcomes, and may decrease student 2 These criticisms included program sampling, implementation status of programs, and time frame for data collection.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.374 | 0.074 |
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".