{"id":"W3162380077","doi":"10.1080/00913367.2021.1909515","title":"Preparing for an Era of Deepfakes and AI-Generated Ads: A Framework for Understanding Responses to Manipulated Advertising","year":2021,"lang":"en","type":"article","venue":"Journal of Advertising","topic":"Sexuality, Behavior, and Technology","field":"Psychology","cited_by":282,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Falsity; Computer science; Originality; Production (economics); Advertising; Generative grammar; Adversarial system; Data science; Artificial intelligence; Business; Sociology; Economics; Epistemology","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":[0.003718208,0.0006979386,0.0003700649,0.001270004,0.001106231,0.004896984,0.001338768,0.002532928,0.006957771],"category_scores_gemma":[0.008712546,0.0004012985,0.0005914944,0.0005686399,0.008423689,0.006404188,0.001858943,0.00381681,0.0006741449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002241498,"about_ca_system_score_gemma":0.001068929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002062616,"about_ca_topic_score_gemma":0.001492527,"domain_scores_codex":[0.9981273,0.001194673,0.00004696558,0.0002790252,0.0002163608,0.0001357511],"domain_scores_gemma":[0.9947065,0.003588753,0.000644415,0.0004911647,0.0003360218,0.0002330723],"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":[0.0000679736,0.00006822123,0.001490043,0.0001197921,0.00002079588,0.0001737648,0.005909381,0.00521921,0.00203426,0.9648504,0.00120917,0.01883702],"study_design_scores_gemma":[0.00002613791,0.00007457373,0.002022104,0.0001453,0.00002137672,0.0001557021,0.003196855,0.04371575,0.0008029985,0.9334174,0.01638426,0.00003760056],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1141955,0.003616502,0.6612511,0.03747281,0.0004567264,0.0003492903,0.0004149952,0.0004089932,0.1818342],"genre_scores_gemma":[0.8959162,0.001268848,0.09175728,0.002429145,0.0001844858,0.0002783478,0.0001043862,0.00009290305,0.007968465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006957771,"threshold_uncertainty_score":0.02327603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1042258973273822,"score_gpt":0.4056259956260836,"score_spread":0.3014000982987015,"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."}}