{"id":"W4413668568","doi":"10.64628/aaj.gacuqufxa","title":"Study provides fresh insights into the benefits of mobile money in Kenya","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"International Development Research Centre","funders":"","keywords":"Mobile payment; Business; Natural resource economics; Environmental economics; Economics; Internet privacy; Computer science; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007003371,0.000328515,0.001012812,0.000364211,0.0002588013,0.000117098,0.001365412,0.0003761376,0.0000970221],"category_scores_gemma":[0.0001486712,0.0002730979,0.0001930656,0.000152368,0.0001279041,0.0001831573,0.002257819,0.0006026966,0.0001376802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001264259,"about_ca_system_score_gemma":0.00008767824,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01902415,"about_ca_topic_score_gemma":0.01225686,"domain_scores_codex":[0.9975659,0.0000268218,0.001256606,0.0007998475,0.00007025068,0.0002805866],"domain_scores_gemma":[0.9969496,0.0000500939,0.001221861,0.00166284,0.00007557037,0.00003999757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001198261,0.003032582,0.6998147,0.0004706381,0.0001881762,0.00003333662,0.08786025,0.004507534,0.0001021877,0.1748827,0.003753923,0.02523414],"study_design_scores_gemma":[0.00206077,0.0007845264,0.622625,0.0006055267,0.00003619136,0.000001327617,0.003136445,0.001046486,0.002143412,0.3068025,0.0591833,0.00157445],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602741,0.01990246,0.0000922305,0.0001575978,0.000754969,0.00179695,0.00005810085,0.00002192507,0.01694165],"genre_scores_gemma":[0.9933639,0.004477426,0.000175509,0.00006336247,0.000151028,0.0003753973,0.00001975045,0.00003452492,0.001339064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1319198,"threshold_uncertainty_score":0.9999721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04209190660355584,"score_gpt":0.2599978953204087,"score_spread":0.2179059887168529,"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."}}