{"id":"W3115720499","doi":"10.1080/00913367.2020.1843090","title":"Artificial Intelligence in Advertising Creativity","year":2020,"lang":"en","type":"article","venue":"Journal of Advertising","topic":"Creativity in Education and Neuroscience","field":"Psychology","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; McGill University","funders":"","keywords":"Creativity; Novelty; Computer science; Set (abstract data type); Process (computing); Advertising research; Advertising; Native advertising; Generative grammar; Advertising campaign; Psychology; Artificial intelligence; Online advertising; The Internet; Business; Social psychology; World Wide Web","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":[0.0004363794,0.0001076523,0.0002451271,0.0001752069,0.00006612669,0.00004693738,0.0002381801,0.00005280156,0.0004944177],"category_scores_gemma":[0.0007964729,0.0001024833,0.00009113253,0.0006102363,0.0000949575,0.0003573741,0.00003326458,0.0003861494,0.00005323295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007307597,"about_ca_system_score_gemma":0.0000954425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002679214,"about_ca_topic_score_gemma":0.00001121922,"domain_scores_codex":[0.9985021,0.0001807682,0.0006303068,0.0001900419,0.0002668781,0.0002299029],"domain_scores_gemma":[0.9990503,0.0002657994,0.0003168891,0.000119127,0.00007714928,0.0001707774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008498129,0.0011673,0.08326185,0.0000395065,0.0000392964,0.0007298836,0.04974373,0.001729882,0.1053247,0.007085665,0.001584979,0.7484434],"study_design_scores_gemma":[0.001286174,0.002918636,0.7919807,0.00110808,0.0001419626,0.001942434,0.03397949,0.01035345,0.1285333,0.01279692,0.01347266,0.001486178],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9492043,0.0002377544,0.03950436,0.004964279,0.001139185,0.00007824512,0.000001238661,0.00001671879,0.004853875],"genre_scores_gemma":[0.9974928,0.00002031512,0.001118004,0.001023835,0.0002787295,7.292993e-7,1.657048e-7,0.00001042229,0.00005500036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7469572,"threshold_uncertainty_score":0.5413527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08169778714923313,"score_gpt":0.3911872114817015,"score_spread":0.3094894243324684,"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."}}