{"id":"W4393443031","doi":"10.1017/eds.2023.44","title":"Ideology from topic mixture statistics: inference method and example application to carbon tax public opinion","year":2024,"lang":"en","type":"article","venue":"Environmental Data Science","topic":"Climate Change Communication and Perception","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Ideology; Inference; Public opinion; Statistics; Political science; Econometrics; Sociology; Economics; Mathematics; Epistemology; Law; Politics; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001072793,0.00007059809,0.00007211043,0.00007786833,0.0003577849,0.0002350825,0.0008185625,0.00005313986,0.0005960547],"category_scores_gemma":[0.0002035942,0.00007070224,0.000005910246,0.0002646574,0.0004019268,0.0006032168,0.0007584468,0.0001075251,0.000123378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002311848,"about_ca_system_score_gemma":0.00006083887,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009549919,"about_ca_topic_score_gemma":0.004438734,"domain_scores_codex":[0.9987055,0.0001441003,0.0001197306,0.0004926486,0.0003295101,0.0002084837],"domain_scores_gemma":[0.99887,0.0003126432,0.00003392024,0.0006237046,0.00000731761,0.0001523798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005208185,0.00006318408,0.02895669,0.00001664999,0.000008440867,0.000001198291,0.02310253,0.000005321697,0.05574158,0.0311598,0.002292293,0.8586471],"study_design_scores_gemma":[0.00006175553,0.00002566224,0.06075214,0.0000162625,0.000007787462,9.925317e-7,0.002397965,0.008893374,0.00008551268,0.003462635,0.9241232,0.000172689],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3783028,0.003518006,0.5460491,0.04021213,0.002857043,0.001915746,0.009474925,0.0003920923,0.01727817],"genre_scores_gemma":[0.9731275,0.005288589,0.01945687,0.000457589,0.0001695831,0.0000341054,0.001192726,0.000006574466,0.0002664503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.921831,"threshold_uncertainty_score":0.9970456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3705096812719827,"score_gpt":0.4768085917294069,"score_spread":0.1062989104574242,"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."}}