{"id":"W2904391995","doi":"","title":"Access to credit markets and informality","year":2018,"lang":"en","type":"article","venue":"","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Collateral; Informal sector; Business; Labour economics; Economics; Finance; Market economy","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003550715,0.00008679812,0.000181015,0.0001196436,0.0001524199,0.0001063446,0.0002072452,0.00006937414,0.001195894],"category_scores_gemma":[0.00007574299,0.00009256745,0.00002746554,0.0002076718,0.00005616221,0.000494528,0.0003907859,0.00005175264,0.001401507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002568703,"about_ca_system_score_gemma":0.000008745444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000324293,"about_ca_topic_score_gemma":0.00009104116,"domain_scores_codex":[0.9992167,0.000002566631,0.000309397,0.0002398189,0.00001766257,0.0002138709],"domain_scores_gemma":[0.9995757,0.00001044807,0.00008355421,0.0002144732,0.00003409892,0.00008173296],"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":[0.0001258311,0.00007044381,0.3298371,0.00003794188,0.0000156872,0.000003314236,0.000988216,4.809449e-7,0.0001703136,0.4633332,0.1703558,0.03506174],"study_design_scores_gemma":[0.0001478811,0.00006039719,0.4308267,0.000005805977,6.422974e-7,0.000001678052,0.000007927583,0.00007540095,0.0007216601,0.01194,0.5560709,0.000141054],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6811273,0.0002024926,0.009916537,0.001105194,0.0004637697,0.0001616458,0.00003895121,0.00003541769,0.3069487],"genre_scores_gemma":[0.9917437,0.0001339007,0.001023558,0.003425357,0.000303662,0.000009423896,0.000003184179,0.000008551938,0.003348697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4513932,"threshold_uncertainty_score":0.9997172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04163353421388855,"score_gpt":0.2645964819584258,"score_spread":0.2229629477445372,"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."}}