{"id":"W2938775607","doi":"10.5267/j.msl.2019.4.009","title":"The contribution of life and non-life insurances on ASEAN economic growth","year":2019,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Economics","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":[0.0007896248,0.0002501681,0.0001858105,0.0009028096,0.0003596744,0.001370185,0.0002106409,0.0001805403,0.001430196],"category_scores_gemma":[0.002236423,0.00007747093,0.0003293888,0.00108936,0.0003697244,0.0007186417,0.0009327639,0.0006780913,0.0001770039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001249155,"about_ca_system_score_gemma":0.001132287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02637316,"about_ca_topic_score_gemma":0.02457273,"domain_scores_codex":[0.9996223,0.000100095,0.00002464728,0.00003893459,0.0001131672,0.0001007165],"domain_scores_gemma":[0.9981645,0.0004913929,0.0005974974,0.00007089454,0.0003883191,0.0002875899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008871296,0.00006771678,0.9643561,0.000043387,0.0001086812,0.001232935,0.0005762819,0.005882214,0.0007850644,0.003295018,0.001247398,0.02231642],"study_design_scores_gemma":[0.000002030517,0.00005365758,0.9794581,0.00003620202,0.00005948995,0.0002518885,0.001701616,0.01187537,0.0005544926,0.0006474636,0.005345652,0.00001402262],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917453,0.0006059101,0.0003103363,0.0005789012,0.00001195629,0.000005329498,0.0003761025,0.000007396495,0.006358868],"genre_scores_gemma":[0.9983256,0.0004955503,0.00006423827,0.00001668939,0.00000851684,0.000002372546,0.0002393801,0.000001298281,0.0008462951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02637316,"threshold_uncertainty_score":0.05243933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008102860063345256,"score_gpt":0.1912867952778036,"score_spread":0.1831839352144583,"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."}}