{"id":"W4402653130","doi":"10.1177/07439156241286499","title":"Generative AI in Marketing: Promises, Perils, and Public Policy Implications","year":2024,"lang":"en","type":"article","venue":"Journal of Public Policy & Marketing","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Generative grammar; Marketing; Business; Public policy; Economics; Computer science; Artificial intelligence; Economic growth","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":[0.02495337,0.0004697523,0.0004403944,0.003746012,0.006053044,0.02203175,0.00205012,0.003170755,0.005538089],"category_scores_gemma":[0.04166983,0.0004846453,0.0007568388,0.004673517,0.03286241,0.01538141,0.008137038,0.00686832,0.0006081681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01210252,"about_ca_system_score_gemma":0.008573006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004520895,"about_ca_topic_score_gemma":0.005057022,"domain_scores_codex":[0.9784047,0.01497031,0.0003935089,0.0009805871,0.00321987,0.002031056],"domain_scores_gemma":[0.9322438,0.05210964,0.004563892,0.005904876,0.00263576,0.002542146],"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":[0.0000674353,0.0002076427,0.01470478,0.0002160743,0.00002772823,0.0001722366,0.024671,0.001112685,0.0004829946,0.9027922,0.001973124,0.05357221],"study_design_scores_gemma":[0.00003957145,0.0001254973,0.02233789,0.0008689379,0.00004813666,0.0002836724,0.08783368,0.004687928,0.001623052,0.7995011,0.08255608,0.00009438832],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3129483,0.00429785,0.03785069,0.1197198,0.0002997747,0.00030442,0.0001432364,0.0003497386,0.5240862],"genre_scores_gemma":[0.9932173,0.0008524028,0.002556677,0.001555086,0.00005823152,0.00006917696,0.00002450174,0.00003604838,0.001630556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02495337,"threshold_uncertainty_score":0.1319676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06893303487195289,"score_gpt":0.3364658684950343,"score_spread":0.2675328336230814,"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."}}