{"id":"W2533716521","doi":"10.5539/cis.v9n4p13","title":"Transforming Governance through Mobile Technology in Developing Nations: Case of Kenya","year":2016,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"ICT Impact and Policies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Broadband; Mobile broadband; Telecommunications; Mobile technology; Developing country; Computer science; Corporate governance; Government (linguistics); Mobile device; Mobile business development; Penetration (warfare); Information and Communications Technology; Business; Mobile Web; Mobile computing; World Wide Web; Economic growth; Wireless; Engineering; Operations research","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000966439,0.0000398243,0.00005370328,0.0001589424,0.00005099753,0.00002006715,0.00008153506,0.00002110139,0.000001904816],"category_scores_gemma":[0.00001277846,0.00002886481,0.000005691312,0.0006174342,0.0001130166,0.003613641,0.0000196817,0.00002229411,0.000003502355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003697609,"about_ca_system_score_gemma":0.00002849972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007529857,"about_ca_topic_score_gemma":0.000003706518,"domain_scores_codex":[0.9996522,0.000001283927,0.0001589604,0.00002075082,0.00006106362,0.0001057237],"domain_scores_gemma":[0.9998447,0.00002473945,0.00002255622,0.00005425024,0.00004154917,0.00001216359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001939051,0.000004520596,0.0009866232,0.0001270545,0.000003286196,0.000001980364,0.02822761,0.003373028,0.002921867,0.2633989,0.00009990612,0.7008532],"study_design_scores_gemma":[0.003123799,0.0003178939,0.02289289,0.001606879,0.000008288052,0.002180645,0.004689953,0.2243987,0.4976758,0.005437652,0.2363964,0.001271044],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7636558,0.0000736767,0.2343769,0.0001172275,0.00009972988,0.00006925143,0.000004051071,0.00003967309,0.001563646],"genre_scores_gemma":[0.9961362,0.0003737903,0.00342421,0.00005039522,0.000006488468,0.000005512317,1.816382e-7,0.00000110969,0.000002054235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6995822,"threshold_uncertainty_score":0.2619802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009363788274738756,"score_gpt":0.2502947812706442,"score_spread":0.2409309929959055,"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."}}