Cairo Evaluation Clinic: Thoughts on Randomized Trials for Evaluation of Development
Bibliographic record
Abstract
We were asked to discuss specific methodological approaches to evaluating three hypothetical interventions. This article uses this forum to discuss three misperceptions about randomized trials. First, nobody argues that randomized trials are appropriate in all settings, and for all questions. Everyone agrees that asking the right question is the highest priority. Second, the decision about what to measure and how to measure it, i.e., through qualitative or participatory methods versus quantitative survey or administrative data methods, is independent of the decision about whether to conduct a randomized trial. Third, randomized trials can be used to evaluate complex and dynamic processes, not just simple and static interventions. Evaluators should aim to answer the most important questions for future decisions, and to do so as reliably as possible. Reliability is improved with randomized trials, when feasible, and with attention to underlying theory and tests of why interventions work or fail so that lessons can be transferred as best as possible to other settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.572 | 0.648 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.034 | 0.045 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".