The Use of Propensity Scores as a Matching Strategy
Bibliographic record
Abstract
Objectives: This study reports on the concept and method of linear propensity scores used to obtain a comparison group from the National Longitudinal Survey of Children and Youth to assess the effects of a longitudinal, structured arts program for Canadian youth (aged 9 to 15 years) from low-income, multicultural communities. Method: This study compares 183 children in a community arts project to 183 children from a national longitudinal survey using propensity score matching. The variables included baseline scores of child-rated conduct problems, indirect aggression, emotional problems, self-esteem, and prosocial behavior and child gender, person most knowledgeable (PMK) education, PMK marital status, household income, and family functioning. Results: Mean score comparison showed that the groups were very similar on all covariates. Conclusions: Propensity score matching offers an alternative to true randomization that is cost-effective and convenient, particularly important for social work research in community-based organizations with a limited budget.
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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 | 0.144 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".