{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001101928,0.0002957264,0.0002040245,0.0009679427,0.008232485,0.002913717,0.0004286892,0.001623081,0.003376969],"category_scores_gemma":[0.001553967,0.0001917302,0.0001744284,0.001681261,0.003732243,0.001945913,0.001780381,0.00130941,0.000181296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006550821,"about_ca_system_score_gemma":0.004338531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08604755,"about_ca_topic_score_gemma":0.2031488,"domain_scores_codex":[0.999113,0.0004513456,0.00002250736,0.00003367277,0.00005885486,0.0003206489],"domain_scores_gemma":[0.9990234,0.0004683719,0.0001853391,0.00002827779,0.0001024741,0.0001922017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004678225,0.001199771,0.1301072,0.0007753529,0.00005243354,0.06312323,0.1635114,0.005889839,0.004657276,0.5803184,0.005970131,0.04392711],"study_design_scores_gemma":[0.0002501909,0.0006917634,0.1464075,0.0015536,0.0001300461,0.005051463,0.701431,0.007197734,0.003310144,0.01934574,0.1144878,0.0001431342],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9576873,0.000579029,0.0003412979,0.002198065,0.00001146502,0.0001127173,0.0000428685,0.000002154268,0.03902517],"genre_scores_gemma":[0.9962677,0.0007539313,0.0002983072,0.0001430353,0.000003296029,0.00003551089,0.00001333636,0.000001027159,0.002483845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08604755,"threshold_uncertainty_score":0.1710933,"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."}}