Investigating the Role of School-Based Extracurricular Activity Participation in Adolescents’ Learning Outcomes: A Propensity Score Method
Bibliographic record
Abstract
The purpose of this study was to apply a propensity score method that could control for selection bias at both the student-level and school-level in an investigation of the causal effect linking participation in school-based extracurricular activity (SBEA) to adolescents’ learning outcomes. The data for this study were drawn from the Education Longitudinal Study of 2002 (ELS: 2002) data set. The final sample comprised 12,247 10th graders; 6,026 (49.20%) were males. A propensity score method incorporating marginal mean weighting through stratification was implemented to analyze the data. Results showed that 10th graders who had proper intensity of participation in SBEA (6–15 hours a week) slightly outperformed peers who did not participate in SBEA on the performance of mathematics achievement in 12th grade. Regarding the link between SBEA participation and adolescents’ long-term learning outcomes, results indicated 10th graders in 2002 with low to moderate levels of intensity (i.e., 1–15 hours) were more likely to achieve higher education credentials by the year 2012 when compared to non-participating peers.
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 | 0.042 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".