{"id":"W3160827071","doi":"10.2139/ssrn.3779113","title":"The Nonmarket Insurance like Effect of CSR on Media Sentiment","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Corporate Identity and Reputation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Nonmarket forces; Business; Sentiment analysis; Chemistry; Monetary economics; Economics; Computer science; Artificial intelligence; Microeconomics","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":[0.0008852746,0.0002008411,0.0003937093,0.0003944564,0.0004038648,0.00162485,0.0002818548,0.001170485,0.01875871],"category_scores_gemma":[0.008337592,0.0001747134,0.0004053682,0.0003324246,0.0006488833,0.0009366808,0.000722564,0.001529723,0.0008924056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004407814,"about_ca_system_score_gemma":0.0004335131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005086053,"about_ca_topic_score_gemma":0.004937573,"domain_scores_codex":[0.9996828,0.0001102185,0.00001370303,0.00006978816,0.00004679483,0.00007673624],"domain_scores_gemma":[0.9873339,0.007159346,0.002497725,0.0005916335,0.0005265879,0.001890795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0105022,0.004352184,0.8537703,0.0003554137,0.0007826436,0.001121444,0.002074418,0.003855912,0.04509349,0.02060218,0.01046729,0.04702254],"study_design_scores_gemma":[0.0001038256,0.0004669512,0.9911389,0.00002454251,0.0001819294,0.00004904677,0.000605363,0.001818265,0.001379999,0.002851433,0.001362012,0.00001761713],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975713,0.0002228801,0.000125601,0.0012682,0.00004512298,0.000007292146,0.0003339569,0.00002130896,0.02226267],"genre_scores_gemma":[0.9980236,0.00005984284,0.00002161638,0.0001362384,0.00003872834,0.000001729161,0.00009318193,0.000004963216,0.001620087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01875871,"threshold_uncertainty_score":0.06275415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006249868647804001,"score_gpt":0.1964343608789457,"score_spread":0.1901844922311417,"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."}}