Bibliographic record
Abstract
Rapid Impact Evaluation offers the potential to evaluate impacts in both ex ante and ex post settings, providing utility for developmental and formative evaluation as well as the usual summative settings. Rapid Impact Evaluation triangulates judgments of three separate groups of experts to assess the incremental change in effects attributable to the program. Three methodological innovations are central to the method: the scenario-based counterfactual, a simplified approach to measuring change in effects, and an interest-based approach to stakeholder engagement. In evaluations to date, Rapid Impact Evaluation has proved to be a cost effective and nimble approach to assessing impacts and does not intrude on design or implementation of the program. By applying recent thinking on use-seeking research emphasizing joint knowledge processes over knowledge products, Rapid Impact Evaluation promotes salience, legitimacy, and credibility with decision makers and key stakeholders. Applications show Rapid Impact Evaluation to be fit for purpose.
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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 | 0.119 | 0.251 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.190 | 0.030 |
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".