{"id":"W7099280657","doi":"","title":"Campaign News and Vote Intentions Analyses Using Manual and Automated Coding of Sentiment in Canadian Newspaper Content","year":2015,"lang":"en","type":"article","venue":"","topic":"Energy and Environment Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Newspaper; Coding (social sciences); Content analysis; Content (measure theory); Framing (construction)","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":[0.00255586,0.0005036559,0.000345291,0.00579642,0.002357027,0.002236667,0.0006789506,0.0003754917,0.003764795],"category_scores_gemma":[0.01515921,0.0003019958,0.0005124559,0.007062892,0.0006525129,0.0007936043,0.0007778462,0.0008246849,0.0008631031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009224462,"about_ca_system_score_gemma":0.01692995,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.944434,"about_ca_topic_score_gemma":0.96934,"domain_scores_codex":[0.9975247,0.000339076,0.0001341799,0.000332636,0.001140327,0.000529168],"domain_scores_gemma":[0.9831973,0.003645048,0.001164614,0.000495313,0.01107865,0.0004190286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000913707,0.0003289886,0.8057802,0.0005803885,0.0003575551,0.0001483674,0.01466754,0.001927005,0.01356453,0.002524543,0.03112736,0.1280797],"study_design_scores_gemma":[0.0000219004,0.00003419286,0.9721069,0.00005315957,0.000134022,0.00002591312,0.003967858,0.007113045,0.003164595,0.0001555928,0.01316665,0.00005610412],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9497112,0.0002320721,0.003699193,0.0003149214,0.00006925695,0.0004399696,0.0236929,0.0003194795,0.0215209],"genre_scores_gemma":[0.9584191,0.0001668536,0.007077193,0.00008276329,0.00004161346,0.0004663127,0.02375499,0.000129679,0.009861631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05556601,"threshold_uncertainty_score":0.1117864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1103817509394783,"score_gpt":0.3183993142658442,"score_spread":0.2080175633263659,"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."}}