The Use of Propensity Scores as a Matching Strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it