{"id":"W4390879603","doi":"10.3390/jrfm17010034","title":"Predicting Financial Inclusion in Peru: Application of Machine Learning Algorithms","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidad del Pacífico; University of the Pacific","keywords":"Financial inclusion; Finance; Machine learning; Financial modeling; Financial services; Artificial intelligence; Population; Economics; Computer science; Sociology","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":[0.002258986,0.0007836292,0.0007103156,0.00284893,0.0005282588,0.001426892,0.0007778147,0.0009600674,0.00172815],"category_scores_gemma":[0.008282738,0.0002189407,0.0006536031,0.001978715,0.000236218,0.0009430863,0.001007819,0.001166985,0.0004213282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006727711,"about_ca_system_score_gemma":0.001011192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01894263,"about_ca_topic_score_gemma":0.01114833,"domain_scores_codex":[0.9994007,0.0003834811,0.00003681733,0.00007735004,0.0000437643,0.00005792963],"domain_scores_gemma":[0.9969682,0.002355989,0.0002349262,0.0000815556,0.0002765252,0.0000827825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003951055,0.001200154,0.3222824,0.0002183617,0.000485136,0.0003859014,0.0003394943,0.4033248,0.0004307155,0.002156151,0.0061724,0.2626093],"study_design_scores_gemma":[0.00002013571,0.00006138974,0.0124049,0.00003144003,0.00002767417,0.00003297386,0.0002176173,0.9848535,0.0001236009,0.001659181,0.0005589571,0.00000861838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8984882,0.002412689,0.087028,0.003138744,0.0001108362,0.0001971806,0.001378815,0.0008691738,0.006376368],"genre_scores_gemma":[0.9685884,0.0006317557,0.02817971,0.0001197322,0.00008182789,0.0001014062,0.001373999,0.00002598109,0.0008971335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01894263,"threshold_uncertainty_score":0.03766471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007990128676635433,"score_gpt":0.2142180269772878,"score_spread":0.2062278983006524,"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."}}