{"id":"W2793981244","doi":"10.1177/1464884917751962","title":"Using parallel content analysis to measure mediatization of politics: The televised leaders’ debates in Canada, 1968–2008","year":2018,"lang":"en","type":"article","venue":"Journalism","topic":"Social Media and Politics","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds de Recherche du Québec-Société et Culture; Université de Montréal","keywords":"Framing (construction); Politics; Newspaper; Journalism; Content analysis; Political science; Media content; Public relations; Media studies; Political communication; Sociology; Social science; Law; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0028319,0.0002710091,0.0002891032,0.01110604,0.005552155,0.003222979,0.0007829085,0.0004043888,0.002917253],"category_scores_gemma":[0.01333337,0.0003059247,0.0002453229,0.01326151,0.002185456,0.001138584,0.001941193,0.0009452669,0.0002801685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04604895,"about_ca_system_score_gemma":0.04742951,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.971813,"about_ca_topic_score_gemma":0.986186,"domain_scores_codex":[0.9981495,0.0002239697,0.00008899679,0.0001786251,0.0009839005,0.0003749732],"domain_scores_gemma":[0.9796714,0.003769912,0.003228676,0.0004585669,0.01117321,0.001698141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004996317,0.0001869512,0.704412,0.0004311858,0.00008924766,0.0005994991,0.1976402,0.0003364221,0.003047755,0.004316334,0.007299714,0.08114109],"study_design_scores_gemma":[0.00001246731,0.00003676514,0.9358988,0.00008909209,0.00002765287,0.00005829199,0.04152006,0.000287547,0.0009604097,0.00015992,0.02091894,0.00002995188],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765072,0.0007288727,0.0003688817,0.0007059345,0.00004124901,0.000159547,0.003409861,0.00002179809,0.0180568],"genre_scores_gemma":[0.989724,0.0005627481,0.0007145869,0.0001128521,0.00005080834,0.0001593441,0.002209719,0.00001922704,0.006446601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04604895,"threshold_uncertainty_score":0.3341101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1574590632703767,"score_gpt":0.3492708800631771,"score_spread":0.1918118167928003,"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."}}