{"id":"W4412693404","doi":"10.3390/jrfm18080413","title":"Predicting Environmental Social and Governance Scores: Applying Machine Learning Models to French Companies","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Corporate governance; Accounting; Business; Artificial intelligence; Psychology; Computer science; Finance","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":[0.002063179,0.0007906054,0.0004525673,0.002424439,0.0002841319,0.001615897,0.0003753357,0.0006191113,0.000865228],"category_scores_gemma":[0.004541207,0.0001242051,0.0007638332,0.001778934,0.0002821373,0.0006745167,0.0004112286,0.000580776,0.000230032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002050689,"about_ca_system_score_gemma":0.0008238283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1201521,"about_ca_topic_score_gemma":0.0727066,"domain_scores_codex":[0.9993637,0.0003071105,0.00003037156,0.0001048465,0.00009529157,0.00009867037],"domain_scores_gemma":[0.996761,0.001905247,0.0004778417,0.0001366362,0.0006093829,0.0001100012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000171086,0.000244079,0.4314207,0.00008939142,0.0002878439,0.0004913702,0.0002158182,0.4932876,0.0006372933,0.001898265,0.002025274,0.06923131],"study_design_scores_gemma":[0.00001161566,0.0001169069,0.1325363,0.00004578108,0.00004541602,0.00005408382,0.000242,0.8637875,0.0006980958,0.001115218,0.001320658,0.00002645697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894022,0.000684947,0.007238105,0.0004874417,0.00002219336,0.00002255623,0.0005810931,0.00009111121,0.001470325],"genre_scores_gemma":[0.9959798,0.0002651658,0.002345244,0.0000247557,0.00002148405,0.00001095514,0.0008699487,0.00000650021,0.0004760565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1201521,"threshold_uncertainty_score":0.2389054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486082110837535,"score_gpt":0.1861320431251141,"score_spread":0.1712712220167388,"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."}}