{"id":"W4414686116","doi":"10.1093/rfs/hhaf081","title":"Dissecting Corporate Culture Using Generative AI","year":2025,"lang":"en","type":"article","venue":"Review of Financial Studies","topic":"Collaboration in agile enterprises","field":"Business, Management and Accounting","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Generative grammar; Stakeholder; Organizational culture; Divergence (linguistics); Generative model; Stock (firearms)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01025609,0.0005290955,0.0003257891,0.008193026,0.001002227,0.005376805,0.00129289,0.0006790804,0.001293664],"category_scores_gemma":[0.02576087,0.0003136807,0.0005507405,0.00403063,0.004667222,0.004277012,0.0025367,0.0008544518,0.0001557936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002722288,"about_ca_system_score_gemma":0.001625295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004839008,"about_ca_topic_score_gemma":0.007495451,"domain_scores_codex":[0.991537,0.006491647,0.0002469478,0.0004869756,0.001057595,0.0001799497],"domain_scores_gemma":[0.9272309,0.06366348,0.003417579,0.003591429,0.001819051,0.0002775366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001903972,0.0003021394,0.1484312,0.002523914,0.0005564608,0.0006737286,0.153302,0.02697609,0.007817578,0.1456195,0.002468612,0.5111383],"study_design_scores_gemma":[0.0000781548,0.0003843568,0.1780069,0.003215464,0.0003685398,0.0008775453,0.2310508,0.1465217,0.009116424,0.3745635,0.05556525,0.0002514703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7776261,0.003193194,0.1768201,0.002464276,0.00005465253,0.0003597889,0.0003145591,0.0002770715,0.03889013],"genre_scores_gemma":[0.962925,0.0007134783,0.03548562,0.0001658124,0.000009572366,0.0001167956,0.0001249023,0.00001928202,0.0004395885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01025609,"threshold_uncertainty_score":0.05423999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05132572225826569,"score_gpt":0.3416579287374125,"score_spread":0.2903322064791468,"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."}}