{"id":"W2917741890","doi":"10.5267/j.msl.2019.2.005","title":"The impact of financial inclusion on income inequality in transition economies","year":2019,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Foundation for Science and Technology Development; World Bank Group","keywords":"Financial inclusion; Inequality; Inclusion (mineral); Economics; Transition (genetics); Economic inequality; Demographic economics; Labour economics; Business; Financial system; Finance; Financial services; Mathematics; 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":[0.0006190189,0.0001788261,0.000183151,0.0007795339,0.0005813614,0.001011005,0.0001811168,0.0002863764,0.001236518],"category_scores_gemma":[0.00264534,0.00007759558,0.0004447128,0.0009891476,0.0004565976,0.0009048475,0.001511004,0.0008419721,0.0001038771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008903074,"about_ca_system_score_gemma":0.0006855407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02208116,"about_ca_topic_score_gemma":0.0274704,"domain_scores_codex":[0.9994955,0.0001469492,0.00003033872,0.00004392651,0.00004970955,0.0002335739],"domain_scores_gemma":[0.9987029,0.0003168391,0.0005724802,0.0000409307,0.000161437,0.000205347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001352987,0.0001119429,0.9785612,0.00003849432,0.0001026699,0.000405035,0.0009299977,0.0023512,0.0001751443,0.002211329,0.0005788434,0.01439892],"study_design_scores_gemma":[0.000003519,0.00009670746,0.9920583,0.00005289034,0.00006436592,0.00006703286,0.002335799,0.003020172,0.0002821557,0.0009313553,0.001079205,0.000008445474],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974658,0.0003098079,0.0002195079,0.0006268814,0.0000120421,0.000004395425,0.0002341519,0.000003434232,0.001124023],"genre_scores_gemma":[0.9996737,0.00007559992,0.0000297661,0.00001782135,0.000006210628,0.000001724008,0.0000852611,4.460489e-7,0.0001094604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02208116,"threshold_uncertainty_score":0.04390526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01405076067039246,"score_gpt":0.2397775653046453,"score_spread":0.2257268046342528,"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."}}