{"id":"W7100608678","doi":"","title":"Experiments for the Poor Insurance Provision in Low-Income Communities Part II: Initial Lessons from Micro-Insurance Experiments for the Poor","year":2000,"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":"","funders":"","keywords":"Microfinance; Product (mathematics); Unit (ring theory); State (computer science); Microinsurance; Financial services; Poverty","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01139116,0.0003208909,0.0005317691,0.0005866147,0.005553763,0.001633333,0.001264704,0.002060022,0.006114074],"category_scores_gemma":[0.02263451,0.0003299617,0.0003461828,0.001014297,0.004200327,0.002224782,0.002540849,0.002652942,0.0004382425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003993754,"about_ca_system_score_gemma":0.004674134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0427459,"about_ca_topic_score_gemma":0.07208596,"domain_scores_codex":[0.9950567,0.00392924,0.00006557675,0.0001445491,0.0003263038,0.0004775811],"domain_scores_gemma":[0.97516,0.01844461,0.001013294,0.001458528,0.001402829,0.0025207],"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":[0.01510013,0.06743395,0.1460809,0.001625871,0.0004349948,0.00335236,0.1622756,0.007304227,0.006548787,0.070363,0.1255106,0.3939696],"study_design_scores_gemma":[0.009453254,0.03976787,0.2991094,0.002497017,0.0005217467,0.00137474,0.2789282,0.01163092,0.01081772,0.1268625,0.2185476,0.000489087],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9559523,0.0008494038,0.001656758,0.02096211,0.0001002597,0.001094638,0.0005811934,0.00005975109,0.01874348],"genre_scores_gemma":[0.9816639,0.001522168,0.003687828,0.002781824,0.00004454334,0.002114873,0.0002708724,0.00003431171,0.007879665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0427459,"threshold_uncertainty_score":0.08499414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06774045033471274,"score_gpt":0.3026644202825827,"score_spread":0.23492396994787,"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."}}