{"id":"W4401629862","doi":"10.1016/j.tncr.2024.200091","title":"Enhancing economic growth through digital financial inclusion: An examination of India","year":2024,"lang":"en","type":"article","venue":"Transnational Corporation Review","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Financial inclusion; Inclusion (mineral); Business; Natural resource economics; Economics; Finance; Financial services; Chemistry; Mineralogy","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":[0.001099538,0.000195488,0.0002284649,0.004722036,0.000510911,0.001804301,0.0003576642,0.0002304004,0.00110444],"category_scores_gemma":[0.002219963,0.00008571966,0.0003139841,0.008746728,0.0007992233,0.0008120415,0.000916971,0.0005479463,0.000117298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002423113,"about_ca_system_score_gemma":0.004084767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03568191,"about_ca_topic_score_gemma":0.04713472,"domain_scores_codex":[0.9994867,0.0001281764,0.00003698485,0.00002459112,0.0001928213,0.0001308249],"domain_scores_gemma":[0.9978605,0.0007620364,0.0005241139,0.00006064578,0.0006317596,0.0001609428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003417009,0.0003384514,0.2650329,0.008511586,0.0006697605,0.002584677,0.00843092,0.003598654,0.001089234,0.1054663,0.0328093,0.5711266],"study_design_scores_gemma":[0.00002866741,0.0003712293,0.7344481,0.003601913,0.0007523501,0.001147255,0.01446349,0.001541038,0.001370129,0.004080027,0.2381422,0.00005357801],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7004874,0.2134077,0.0005415257,0.01419057,0.0002561111,0.00007203354,0.0008315245,0.00003637749,0.07017677],"genre_scores_gemma":[0.9041546,0.09333424,0.0002159678,0.0005392621,0.0001029527,0.00001124285,0.0002000059,0.000004379978,0.00143741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03568191,"threshold_uncertainty_score":0.07094842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03175075962274557,"score_gpt":0.2572262335201427,"score_spread":0.2254754738973972,"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."}}