{"id":"W3034784053","doi":"10.3390/jrfm13060122","title":"Microfinance Participation in Thailand","year":2020,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Microfinance; Loan; Multinomial logistic regression; Household income; Business; Socioeconomic status; Demographic economics; Inequality; Survey data collection; Dependency ratio; Financial services; Economics; Labour economics; Socioeconomics; Economic growth; Finance; Geography; Population","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":[0.0005218136,0.0001262119,0.0004159315,0.0002017764,0.00007962145,0.0000324995,0.0001594131,0.00007516111,0.00002798791],"category_scores_gemma":[0.0001484831,0.0001324165,0.00008867568,0.0003615585,0.00003738737,0.0002438659,0.00009780657,0.0002291001,0.00005323572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004209957,"about_ca_system_score_gemma":0.00001265453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005856959,"about_ca_topic_score_gemma":0.0000209565,"domain_scores_codex":[0.9986781,0.00001645118,0.0008227254,0.0002180713,0.00004900086,0.0002156007],"domain_scores_gemma":[0.9991547,0.00001941264,0.0006223656,0.00009626712,0.00002878825,0.00007848214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005089231,0.0002935749,0.636317,0.0001641865,0.00001895723,0.0002518901,0.005647091,0.0002543079,0.0001254971,0.1564757,0.004958033,0.1949849],"study_design_scores_gemma":[0.001608016,0.0002419588,0.5892402,0.00007052724,0.00001460541,0.000004610431,0.0000819312,0.0001630981,0.0001172865,0.02445264,0.3837858,0.0002193483],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9152283,0.009149552,0.07106961,0.0008461006,0.0004209953,0.0001963708,0.00003557177,0.000007592885,0.003045918],"genre_scores_gemma":[0.9868323,0.01067443,0.001648077,0.0005492859,0.0002167115,0.000004256723,0.000001309609,0.00001046296,0.00006320177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3788278,"threshold_uncertainty_score":0.5399789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01930313308333639,"score_gpt":0.222179429180014,"score_spread":0.2028762960966776,"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."}}