{"id":"W4390355411","doi":"10.22495/cgobrv7i4sieditorial","title":"Editorial: Pairing old with the new: Firm performance, ESG, and big data","year":2023,"lang":"en","type":"editorial","venue":"Corporate Governance and Organizational Behavior Review","topic":"Business and Economic Development","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Pairing; Business; Big data; Computer science; Data mining; Physics; Condensed matter physics","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":[0.01170502,0.004399992,0.006128088,0.005683606,0.006236464,0.01217943,0.005539077,0.02393484,0.01791843],"category_scores_gemma":[0.04880673,0.001619558,0.003644695,0.00330158,0.003848232,0.006102847,0.002130697,0.02722188,0.01596477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004093931,"about_ca_system_score_gemma":0.0067534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003466805,"about_ca_topic_score_gemma":0.01097335,"domain_scores_codex":[0.9911932,0.001598426,0.0009956638,0.001155672,0.004507741,0.0005493103],"domain_scores_gemma":[0.951949,0.0228197,0.002514768,0.001281194,0.01610917,0.005326095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001931315,0.000008577467,0.00001738305,0.000085044,0.00001172329,0.00004396932,0.000007456818,0.00001315548,0.00002351296,0.0001712595,0.9977922,0.001806309],"study_design_scores_gemma":[0.0001463285,0.00004143453,0.0004762457,0.0009204118,0.00008316941,0.0002235508,0.00007282453,0.0002946798,0.0001165631,0.002598265,0.9949853,0.00004117907],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00002253642,0.002087438,0.00009477048,0.04258742,0.9540809,0.00002482944,0.00007682948,0.0000543791,0.0009708366],"genre_scores_gemma":[0.0002388176,0.001764996,0.00009002242,0.02755,0.9660144,0.00002960773,0.00003552294,0.00004078065,0.004235876],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.02393484,"threshold_uncertainty_score":0.06190282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03199633162256537,"score_gpt":0.2201750369780769,"score_spread":0.1881787053555116,"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."}}