Local knowledge, formal evidence, and policy decisions
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
How do policymakers value advice from local experts versus formal evidence from impact evaluations when making policy decisions? Using a discrete choice experiment conducted in collaboration with the World Bank and Inter-American Development Bank, we show that policymakers were willing to accept a program that had a 5.0 percentage point smaller estimated effect on enrollment rates if it were recommended by a local expert. They also preferred programs supported by evidence from a different region over programs supported by local evaluations only if the former had a 5.8 percentage point higher estimated impact. These premiums are large, surpassing the effects of many programs aimed at improving enrollment rates. This highlights the substantial weight that policymakers place on local evidence. • Policymakers and policy practitioners prefer programs with a local impact evaluation. • They also prefer programs recommended by local experts. • These preferences often outweigh differences in estimated treatment effects.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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