{"id":"W4385402364","doi":"10.3905/jesg.2023.1.079","title":"ESG Risk Ratings: Quantitative Insights Benefiting from Size and Sector Adjustments","year":2023,"lang":"en","type":"article","venue":"The Journal of Impact and ESG Investing","topic":"Corporate Social Responsibility Reporting","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Corporate governance; Business; Investment (military); Finance","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002011819,0.000200325,0.0003459294,0.0001853756,0.0006243254,0.0003609477,0.0001454329,0.00005777176,0.00001354193],"category_scores_gemma":[0.01372643,0.0001298004,0.00008750461,0.0006791827,0.0001230833,0.001132586,0.0002238381,0.0003795889,0.000007483036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004372749,"about_ca_system_score_gemma":0.00007946769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001808941,"about_ca_topic_score_gemma":0.0001643812,"domain_scores_codex":[0.9984384,0.0001093666,0.0006993478,0.0001494707,0.0003104423,0.0002930207],"domain_scores_gemma":[0.9949727,0.00238418,0.002169013,0.0001248236,0.0002959612,0.00005332759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004621399,0.00003639084,0.934355,0.0001358408,0.0003567348,0.0002293474,0.01388613,0.0008457128,0.01947732,0.0003744182,0.0004002108,0.02944075],"study_design_scores_gemma":[0.0009304538,0.0001085519,0.9225241,0.0005123306,0.0003461938,0.00006884908,0.01847311,0.01450044,0.0002141674,0.04194518,0.00007423862,0.0003024052],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982284,0.0006189602,0.00004688274,0.0002479878,0.0001773454,0.0001376195,0.000002314544,0.00005244958,0.0004880151],"genre_scores_gemma":[0.9984045,0.0001095451,0.0003325696,0.0002847991,0.0008103549,7.501108e-7,0.000001834814,0.00002688974,0.00002873088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04157076,"threshold_uncertainty_score":0.9945814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04757245329253627,"score_gpt":0.2877062302904651,"score_spread":0.2401337769979288,"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."}}