{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002955409,0.0004884048,0.0003861457,0.001882746,0.001076303,0.006294127,0.001147836,0.001615324,0.003167889],"category_scores_gemma":[0.008477777,0.0003403674,0.00100642,0.001307632,0.006788411,0.004304382,0.002299853,0.00173617,0.0006115075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001306382,"about_ca_system_score_gemma":0.001057934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008616323,"about_ca_topic_score_gemma":0.0005958104,"domain_scores_codex":[0.9979149,0.0009958853,0.0001935946,0.0003545095,0.0004322182,0.0001090281],"domain_scores_gemma":[0.9929652,0.005057953,0.0004061159,0.0008939021,0.0004425917,0.0002341604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00007377557,0.00009468418,0.00318871,0.0002680697,0.00006031605,0.0002726438,0.002315283,0.01332821,0.001800801,0.8505653,0.003039694,0.1249925],"study_design_scores_gemma":[0.00005706423,0.00007710707,0.001866184,0.0001155507,0.00003693413,0.0004184436,0.0005724748,0.06871744,0.002065423,0.895197,0.03081863,0.00005768085],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1430251,0.006507137,0.5851972,0.01367391,0.0006108096,0.0003982061,0.0001677525,0.001269986,0.2491499],"genre_scores_gemma":[0.762536,0.001453333,0.2230672,0.001059558,0.0002326722,0.0002300949,0.0001290577,0.00009784033,0.01119421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006294127,"threshold_uncertainty_score":0.01562989,"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."}}