{"id":"W2153423075","doi":"10.1136/tc.2005.013854","title":"Every document and picture tells a story: using internal corporate document reviews, semiotics, and content analysis to assess tobacco advertising","year":2006,"lang":"en","type":"article","venue":"Tobacco Control","topic":"Media Studies and Communication","field":"Social Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Cancer Institute; Flight Attendant Medical Research Institute","keywords":"Tobacco industry; Semiotics; Advertising; Content analysis; Promotion (chess); Computer science; Sociology; Business; Political science; Linguistics; Social science","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":[0.001490393,0.0002034359,0.0005706278,0.0001302183,0.000580901,0.0002605944,0.0002344298,0.00007787374,0.0000533163],"category_scores_gemma":[0.000208492,0.0001752141,0.0001134161,0.000344911,0.0001421548,0.0002470704,0.0001537554,0.0001695139,0.000005682166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003020648,"about_ca_system_score_gemma":0.00006128678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006546235,"about_ca_topic_score_gemma":0.009927405,"domain_scores_codex":[0.9979565,0.0005631043,0.0004364787,0.0003339657,0.0003521981,0.0003578047],"domain_scores_gemma":[0.9986942,0.0002401068,0.0003888622,0.0003265689,0.0001652939,0.0001849744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001575502,0.00008821656,0.9297832,0.0000319878,0.0009501419,0.00001277797,0.004337572,0.0005918312,0.001496639,0.005467676,0.002484162,0.05459831],"study_design_scores_gemma":[0.007605267,0.0004710967,0.5919184,0.001154064,0.007264837,0.00001196744,0.02640888,0.001929657,0.0002741031,0.00268532,0.3576383,0.002638145],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676375,0.01709295,0.008072265,0.004890568,0.0003077625,0.00114712,0.00001103746,0.00004263623,0.0007981074],"genre_scores_gemma":[0.9926631,0.002902462,0.002276727,0.001249333,0.0002466499,0.00003174875,0.00000598276,0.00001035974,0.0006136525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3551541,"threshold_uncertainty_score":0.9895993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06676342098341588,"score_gpt":0.321541658656726,"score_spread":0.2547782376733101,"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."}}