{"id":"W2096261107","doi":"10.5539/ass.v8n6p156","title":"Political Cartoons as a Vehicle of Setting Social Agenda: The Newspaper Example","year":2012,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Humor Studies and Applications","field":"Psychology","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Newspaper; Politics; Sociology; Media studies; Content analysis; Period (music); Mirroring; Set (abstract data type); Nonprobability sampling; Connotation; Social media; Psychology; Social science; Public relations; Political science; Linguistics; Aesthetics; Law; Computer science; Communication","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001771142,0.0004231443,0.0001898696,0.0018568,0.006139253,0.004667989,0.0003625327,0.0009371548,0.00410573],"category_scores_gemma":[0.003650982,0.0002257986,0.0002084412,0.002772052,0.005073055,0.003557465,0.00177783,0.001350944,0.000521465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001793913,"about_ca_system_score_gemma":0.0007870694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003809875,"about_ca_topic_score_gemma":0.008458299,"domain_scores_codex":[0.9981218,0.001446888,0.00003741618,0.00006722732,0.0001887581,0.0001379739],"domain_scores_gemma":[0.9958447,0.003198656,0.000236326,0.0001851025,0.0003469482,0.0001881979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003198677,0.0001295651,0.007289117,0.001184069,0.00002343665,0.0100133,0.6390739,0.0004167477,0.00546706,0.2179833,0.02601768,0.09208186],"study_design_scores_gemma":[0.00001801302,0.00009083516,0.007359704,0.0008974287,0.00002907247,0.001796376,0.2697511,0.0007943888,0.002958232,0.01087998,0.7053825,0.00004234876],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5628157,0.008505032,0.01154046,0.0173889,0.002006128,0.0001957384,0.000287603,0.00008952897,0.3971708],"genre_scores_gemma":[0.9614401,0.004095783,0.006595732,0.001103926,0.0003945638,0.00006991149,0.0001064373,0.00008031706,0.02611326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006139253,"threshold_uncertainty_score":0.013735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05540434046831807,"score_gpt":0.3879992332626951,"score_spread":0.3325948927943771,"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."}}