{"id":"W4408363048","doi":"10.23977/jaip.2025.080108","title":"AI for Financial Inclusion: Bailing out the Unbanked in China","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Unbanked; Financial inclusion; China; Inclusion (mineral); Business; Economics; Financial system; Financial services; Political science; Finance; Sociology; Social science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002948249,0.0001896826,0.0003061473,0.0004830704,0.0005192763,0.0005785382,0.0006994579,0.0001072617,0.00003680737],"category_scores_gemma":[0.01504312,0.0001463122,0.0001793982,0.001066807,0.0001119633,0.00360092,0.0005291178,0.0006807331,0.00006053293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001216432,"about_ca_system_score_gemma":0.0002193399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001553226,"about_ca_topic_score_gemma":0.00038746,"domain_scores_codex":[0.9979688,0.00003168186,0.001074199,0.0002213644,0.0003667769,0.0003371682],"domain_scores_gemma":[0.9971642,0.0008314535,0.001028485,0.0002039986,0.0007591561,0.00001266492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002582493,0.0007203507,0.0008563396,0.0002189326,0.0000588818,0.0001119717,0.001387367,0.003097137,0.002217995,0.6471715,0.01193153,0.3296455],"study_design_scores_gemma":[0.0001989324,0.00009871185,0.0008622401,0.0005704568,0.000141704,0.00002352791,0.001542861,0.01965477,0.005878584,0.4021887,0.5685126,0.0003268417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1322347,0.001174037,0.6553465,0.1688781,0.009146703,0.001368767,0.00000542922,0.00007925782,0.03176655],"genre_scores_gemma":[0.9886119,0.00004200784,0.001357797,0.008217759,0.001449018,0.00001265999,0.000001279076,0.00001793487,0.0002896129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8563772,"threshold_uncertainty_score":0.9932536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03358852790797719,"score_gpt":0.3334793679498073,"score_spread":0.2998908400418301,"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."}}