{"id":"W4206929308","doi":"10.1016/j.jdeveco.2026.103852","title":"Making Information Actionable: Experimental Evidence from Kenyan Courts","year":2020,"lang":"en","type":"dataset","venue":"Journal of Development Economics","topic":"Historical Studies in Central America","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Kenya; Internet privacy; Data science; Political science; Business; Computer science; Law","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001354099,0.0002374631,0.0004769503,0.0001109239,0.0003031928,0.0002606006,0.0004305938,0.00007954256,0.002297571],"category_scores_gemma":[0.0001021745,0.0002186365,0.0001113862,0.00001835871,0.00009018202,0.001034172,0.0001643311,0.0004044627,0.0005496129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001657878,"about_ca_system_score_gemma":0.0004525454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006366969,"about_ca_topic_score_gemma":0.00005113582,"domain_scores_codex":[0.998375,0.00002337912,0.001065987,0.0001277618,0.0002151596,0.000192733],"domain_scores_gemma":[0.9979616,0.0001294089,0.001566382,0.0001113833,0.0001262669,0.0001049994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005990917,0.00003655179,0.000005573344,0.00002664015,0.0002190776,0.000007723177,0.006644775,0.0000238324,6.471421e-7,0.00004465016,0.9908056,0.002125024],"study_design_scores_gemma":[0.0001644944,0.00007429515,0.00001926612,0.0001995232,0.00004241522,0.00000622917,0.001260361,0.000005643384,0.00002866227,0.00002890438,0.99792,0.0002502382],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002374992,0.001835424,0.00008473791,0.001000354,0.01576956,0.0003029841,0.9765258,0.00002955845,0.002076606],"genre_scores_gemma":[0.006670495,0.00964113,0.009554951,0.006246324,0.0122451,0.00004148798,0.9549658,0.00007797315,0.0005566985],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02155995,"threshold_uncertainty_score":0.9986145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07730802709552528,"score_gpt":0.2625476512302246,"score_spread":0.1852396241346993,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}