{"id":"W7008901855","doi":"","title":"Data Analytics, The Next Frontier: Taking a Byte Out of Corruption","year":2017,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Language change; Enforcement; Law enforcement; Analytics; Civil society","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002031375,0.0001257136,0.0001896525,0.00003101714,0.0002652882,0.000217939,0.001367169,0.00009817695,0.00009782336],"category_scores_gemma":[0.0002528117,0.0000979789,0.00005061601,0.00003515733,0.0001942133,0.0005071669,0.0004229072,0.0002430363,0.00007379925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000284123,"about_ca_system_score_gemma":0.00001111811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004819374,"about_ca_topic_score_gemma":0.001633596,"domain_scores_codex":[0.9992386,0.0000100185,0.0002041296,0.0001747926,0.0001536936,0.0002188164],"domain_scores_gemma":[0.9976195,0.00003612896,0.0001365354,0.002138138,0.00003393537,0.00003575506],"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":[0.0001185542,0.0001807378,0.138747,0.0008933886,0.001898531,0.0001003434,0.001288831,0.01268069,0.01512014,0.2195536,0.3758428,0.2335754],"study_design_scores_gemma":[0.0008796178,0.00006860925,0.042262,0.0002624005,0.0001817998,0.000007787003,0.001498523,0.1081822,0.005217716,0.005434425,0.835355,0.0006499097],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8551648,0.008103843,0.02037865,0.002386608,0.00939936,0.0008255198,0.0007266639,0.002069352,0.1009452],"genre_scores_gemma":[0.9978043,0.0003215068,0.00116326,0.00004763648,0.000217582,0.000006588759,0.00002652301,0.0000235517,0.0003890858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4595122,"threshold_uncertainty_score":0.3995463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06674270225713465,"score_gpt":0.2752906052173562,"score_spread":0.2085479029602216,"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."}}