Scaling Up Programs: Reflections on the Importance of Process Evaluation
Bibliographic record
Abstract
Abstract: For more than a decade, policy makers and funding agencies have been focused on identifying innovative and successful programs and bringing them to scale. Evaluators play an important role in these scaling efforts by helping to document what works and by monitoring program implementation. They can also monitor the replication of taking innovative programs to scale. In this research and practice note, we reflect on our evaluation experiences with a public-private partnership designed to scale up a health and wellness program within a large, urban school district at ten elementary schools. In doing so, we highlight the importance of conducting a process evaluation at the beginning of the program to ensure that the program is being implemented as intended. We also describe how these early evaluation findings helped to improve the program during its second year.
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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 | 0.590 | 0.467 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.017 | 0.048 |
| Scholarly communication | 0.029 | 0.032 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.016 | 0.040 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".