Making Information Actionable: Experimental Evidence from Kenyan Courts
Bibliographic record
Abstract
We conduct a randomized controlled trial across Kenyan courts to tackle a major source of court delays: adjournments, i.e., postponements of cases to later dates without reaching a resolution. Partnering with the Kenyan Judiciary, we introduce a simple, cost-effective one-page document that outlines the main reasons for adjournments in each court. We test this intervention in two experimental arms: one where the information is shared with judges, and the other where it is shared with both judges and court user committees. Our intervention successfully prevents approximately 20,000 adjournments nationwide, with each adjournment previously delaying cases by an average of 3 months. The acceleration of court proceedings leads to a notable increase in the demand for and utilization of the legal system, increasing the filing of cases as well as citizen trust in the justice system. Our findings demonstrate that tackling the underlying reasons for court delays can substantially enhance both the perceived and actual performance of judicial systems, fostering greater usage and trust in legal institutions.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.017 |
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".