{"id":"W4387454750","doi":"10.3390/risks11100175","title":"Microinsurance and Economic Growth in Africa","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Microinsurance; Economics; Nexus (standard); Development economics; Poverty; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004246417,0.0002159119,0.0001438503,0.001112093,0.0003939921,0.001335961,0.0001286322,0.0002789853,0.00254376],"category_scores_gemma":[0.002578616,0.0000925161,0.0001780313,0.001852446,0.0008357036,0.0009589334,0.001269709,0.0006998905,0.000150327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009302544,"about_ca_system_score_gemma":0.0004635155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005591905,"about_ca_topic_score_gemma":0.007474823,"domain_scores_codex":[0.9998333,0.0000558783,0.000008235575,0.00002069926,0.00002289976,0.00005888128],"domain_scores_gemma":[0.9979221,0.0006236953,0.001104932,0.00003395459,0.00007714064,0.0002382139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001960863,0.0001134463,0.8576612,0.000209462,0.0001386794,0.001670379,0.004875006,0.007297794,0.0006561789,0.04663647,0.002848608,0.07769676],"study_design_scores_gemma":[0.00001029411,0.00008172296,0.9576761,0.000390884,0.00004774524,0.000460023,0.005146677,0.005161915,0.0004646439,0.01464858,0.01589068,0.00002088434],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821991,0.00684325,0.0003448666,0.003608322,0.00002065769,0.000006516915,0.0002516267,0.000007301766,0.006718507],"genre_scores_gemma":[0.9967914,0.002640954,0.00008211604,0.00002978033,0.0000179829,0.000002667707,0.00003563821,0.000001337835,0.0003981135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005591905,"threshold_uncertainty_score":0.01111871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0339129592626693,"score_gpt":0.2359882357856747,"score_spread":0.2020752765230054,"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."}}