{"id":"W1567232187","doi":"","title":"TELEVISION ADVERTISING IN MAYORAL CAMPAIGNS","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Gender Politics and Representation","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Politics; Presentation (obstetrics); Advertising; Political advertising; Political science; Television advertising; Media studies; Sociology; Business; Law; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001118759,0.0001195934,0.0001622507,0.001034152,0.001964961,0.00357025,0.000240069,0.0009666484,0.01948476],"category_scores_gemma":[0.005649318,0.0001877185,0.0001490799,0.001912267,0.0009724286,0.001152876,0.001020144,0.0009898702,0.0009471027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003237243,"about_ca_system_score_gemma":0.001400002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0330967,"about_ca_topic_score_gemma":0.05452061,"domain_scores_codex":[0.9986908,0.0006547777,0.00002765628,0.00007294205,0.0001680489,0.0003857747],"domain_scores_gemma":[0.9974247,0.00100908,0.0007769776,0.00008611984,0.0002653288,0.0004378188],"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":[0.001365436,0.001112793,0.2627164,0.0004104027,0.00009912507,0.001301156,0.06949778,0.0005671885,0.001917814,0.2613512,0.1340796,0.2655812],"study_design_scores_gemma":[0.00004901206,0.0001362241,0.7411755,0.0004431124,0.00006619175,0.0003058522,0.02758816,0.0005269464,0.0003534335,0.0109501,0.2183799,0.00002569159],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6174557,0.00960768,0.0001778987,0.01705155,0.0002456009,0.00001638808,0.0003171024,0.00002009722,0.355108],"genre_scores_gemma":[0.9867889,0.0009049274,0.00002985158,0.0003664019,0.0001633538,0.000004436932,0.00004779368,0.000008314742,0.01168594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0330967,"threshold_uncertainty_score":0.06580812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563454761509233,"score_gpt":0.3236525617816183,"score_spread":0.308018014166526,"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."}}