{"id":"W4309796935","doi":"10.1371/journal.pone.0273977","title":"The Five Canadas of Climate Change: Using audience segmentation to inform communication on climate policy","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Climate Change Communication and Perception","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council","keywords":"Climate change; Segmentation; Geography; Data science; Computer science; Biology; Artificial intelligence; Ecology","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":[0.005383235,0.0005082008,0.0003963762,0.003286405,0.004698176,0.00400991,0.0009292371,0.0009057007,0.00378704],"category_scores_gemma":[0.02063498,0.0003316171,0.0004691426,0.002097812,0.002465785,0.002514528,0.003452288,0.001499061,0.0003409896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01216855,"about_ca_system_score_gemma":0.01447495,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7307841,"about_ca_topic_score_gemma":0.7725794,"domain_scores_codex":[0.9964436,0.001266293,0.0001197462,0.0003527862,0.00103998,0.0007776752],"domain_scores_gemma":[0.9904051,0.003689747,0.001997752,0.0003475995,0.002287523,0.001272301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005651185,0.0001722688,0.5356861,0.0003690461,0.0001167405,0.0002542758,0.3973578,0.0002855351,0.002701822,0.002680014,0.00254575,0.05726551],"study_design_scores_gemma":[0.00002685264,0.000144439,0.7630036,0.0003219603,0.0001037693,0.00006657027,0.2195502,0.001264518,0.0007297253,0.002008149,0.01267853,0.0001016669],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798688,0.000388444,0.001234901,0.001434937,0.00003799807,0.0001482934,0.0003961613,0.00002424212,0.01646628],"genre_scores_gemma":[0.9971334,0.0002415813,0.0008962575,0.0003274935,0.00001781914,0.00009628446,0.0002104009,0.00001265924,0.00106411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2692159,"threshold_uncertainty_score":0.5416028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5341186958778069,"score_gpt":0.4532444079516972,"score_spread":0.08087428792610968,"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."}}