{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007609218,0.0002046265,0.0003845256,0.0002690868,0.0005016034,0.0002333442,0.0001523545,0.0001160521,0.00005280874],"category_scores_gemma":[0.0001352596,0.0001928595,0.0001630205,0.0006644031,0.00004705008,0.0005451704,0.00004308196,0.001011528,0.00007595285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005724609,"about_ca_system_score_gemma":0.0003966594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005357529,"about_ca_topic_score_gemma":0.000681879,"domain_scores_codex":[0.9976115,0.00002686769,0.0007043614,0.0003908152,0.00006934234,0.001197127],"domain_scores_gemma":[0.9986216,0.00001879115,0.0009925332,0.0002002086,0.00008854277,0.00007829413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000220736,0.00003759516,0.03370702,0.000004441902,0.0003309769,0.000105199,0.0007298663,0.00107832,0.001032898,0.9620141,0.0006699364,0.0002675268],"study_design_scores_gemma":[0.0002197813,0.0001012083,0.006383191,0.00002149678,0.00001778425,0.00007791373,0.001234061,0.004162438,0.0001281052,0.9845567,0.002823155,0.0002741985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830053,0.0067213,0.003334631,0.0002771352,0.001058885,0.0001101755,0.00002074103,0.00006166537,0.005410195],"genre_scores_gemma":[0.9938517,0.002691449,0.0001550557,0.0001813049,0.0007678356,0.000004546954,0.00004766878,0.00003103336,0.002269436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02732382,"threshold_uncertainty_score":0.7864583,"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."}}