The impact of active case management on adjournments, court delays and income: Evidence from Kenya
Bibliographic record
Abstract
An efficient judicial system is a key driver of economic growth, by enforcing contracts and securing property rights.Many judiciaries face problems with delays caused by excessive adjournments which can be costly for citizens involved in litigation.In 2016, the Kenyan judiciary launched an innovative pilot in 11 courts called Active Case Management (ACM), whereby judges must take active control of their cases by organizing pre-trial conferences and setting up deadlines with the overall objective of reducing adjournments.While 18 percent of all hearings ended with an adjournment before the pilot, this figure essentially dropped to zero after the pilot.We find an increase in speed and no decrease in the quality of legal processes.Income of citizens involved in contract enforcement and property rights disputes increased by 34% in the pilot courts.Le système judiciaire joue un rôle important dans la croissance économique car il fait respecter les contrats et aide à garantir les droits de propriété.De nombreux systèmes judiciaires sont confrontés à des problèmes de retards dus à des ajournements excessifs qui peuvent être coûteux pour les citoyens impliqués dans des litiges.En 2016, la justice kenyane a lancé un projet pilote innovant dans 11 tribunaux appelé "Active Case Management" (ACM).Le projet avait comme but d'encourager les juges à prendre un contrôle plus actif de leurs procès en organisant des conférences préparatoires et en fixant des délais avec l'objectif global de réduire les ajournements.Alors que 18% de toutes les audiences se terminaient par un ajournement avant le pilote, ce chiffre est essentiellement tombé à zéro après le pilote.On constate une augmentation de la vitesse et aucune diminution de la qualité des procédures judiciaires.Les revenus des citoyens impliqués dans des litiges relatifs aux contrats et aux droits de propriété ont augmenté de 34% dans les tribunaux ciblés.
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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".