{"id":"W2109414385","doi":"","title":"BANKING TECHNOLOGY TO SCALE MICROFINANCE : THE CASE OF CORRESPONDENT BANKING IN BRAZIL","year":2008,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Microfinance; Financial services; Information and Communications Technology; Business; Population; Retail banking; Business model; Financial inclusion; Mobile banking; Conceptual framework; Loan; Finance; Marketing; Computer science; Economics; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002366092,0.00008413949,0.0003542955,0.0004981383,0.0002759916,0.00004000548,0.0003538969,0.0001385895,0.000003671987],"category_scores_gemma":[0.0005552188,0.00006430494,0.0001536363,0.000787399,0.00002287868,0.0005861392,0.00008041318,0.0001842892,0.00002707599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005394691,"about_ca_system_score_gemma":0.00006222542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001536919,"about_ca_topic_score_gemma":0.00004266708,"domain_scores_codex":[0.9981447,0.00002735435,0.001500047,0.00006621758,0.00008539432,0.0001763201],"domain_scores_gemma":[0.9959632,0.0001043809,0.003385721,0.0001873675,0.0003419758,0.00001735686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002917716,0.0001512569,0.7170787,0.0002163457,0.0001102742,0.0000200894,0.02981416,0.01191015,0.00126023,0.2052671,0.0292478,0.004632154],"study_design_scores_gemma":[0.003954221,0.0003459211,0.1640347,0.0007215857,0.00002943492,0.00190436,0.003953104,0.003054698,0.005162964,0.01040002,0.8059123,0.0005266645],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863216,0.0007517879,0.006814811,0.001566604,0.001841533,0.0005247269,0.00008220954,0.000005439924,0.002091233],"genre_scores_gemma":[0.9989395,0.00008271357,0.0001726744,0.0001992948,0.00008421266,0.00001628283,0.000001290534,0.000005922276,0.0004981016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7766645,"threshold_uncertainty_score":0.2622279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01512020904419151,"score_gpt":0.2405909741009561,"score_spread":0.2254707650567646,"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."}}