Health Programs Uncovered: Simplifying Evaluative Research Decisions with Logic Analysis
Bibliographic record
Abstract
Evaluation researchers and consultants are frequently presented with the challenge of deciding whether to evaluate a health program in advanced stages of implementation, given the investment of time and resources required of a research evaluative project.In this article, we explore a practical solution to this problem by introducing the logic analysis methodology as an alternative approach to support evaluators determine if a program deserves to be evaluated.By providing a transparent and effective method of communication to all parties involved, this approach can help prevent the wastage of valuable resources.Moreover, such approach has great potential to collaborate with the improvement of programs from their initial conception phase up to more advanced stages.
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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 | 0.232 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".