{"id":"W4386350524","doi":"10.2139/ssrn.4558295","title":"Dissecting Corporate Culture Using Generative AI – Insights from Analyst Reports","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Organizational culture; Generative grammar; Mergers and acquisitions; Business; Profitability index; Shareholder value; Equity (law); Value (mathematics); Shareholder; Marketing; Corporate governance; Economics; Management; Political science; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00387106,0.0002617585,0.0001996777,0.003411957,0.0006801144,0.005666232,0.0008758181,0.0008397428,0.001658532],"category_scores_gemma":[0.03281124,0.0003075298,0.0003136526,0.002770116,0.002287089,0.004080344,0.00128518,0.001021781,0.0003350271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232903,"about_ca_system_score_gemma":0.0008990712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005317214,"about_ca_topic_score_gemma":0.005238459,"domain_scores_codex":[0.9973815,0.001653114,0.0001104982,0.0001983809,0.0005385035,0.000118001],"domain_scores_gemma":[0.9511179,0.04088279,0.003190951,0.002406433,0.002023722,0.0003781642],"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":[0.0003265362,0.0003513397,0.2999498,0.0003793776,0.0002436199,0.0011447,0.1457295,0.01584675,0.008485429,0.2151956,0.004709642,0.3076377],"study_design_scores_gemma":[0.00006191518,0.0002251809,0.2696165,0.0004041499,0.000173297,0.001186132,0.1044957,0.2186048,0.005725468,0.3627418,0.0365572,0.0002077952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8862897,0.0006069289,0.05840549,0.002893758,0.00005622247,0.00008390388,0.0003978432,0.0002737801,0.05099241],"genre_scores_gemma":[0.9915823,0.0001502965,0.007281971,0.00009171126,0.00002291852,0.00002370103,0.0001421278,0.0000373539,0.0006675353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005666232,"threshold_uncertainty_score":0.02047235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03796470633173607,"score_gpt":0.2388908600223655,"score_spread":0.2009261536906295,"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."}}