{"id":"W4231107670","doi":"10.32920/ryerson.14645787","title":"Social network analysis of climate change discussion on Twitter during COP21","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Climate Change Communication and Perception","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Skepticism; Climate change; Social media; Sample (material); Microblogging; Political science; Geography; Computer science; World Wide Web; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0007855424,0.0002183615,0.0002214791,0.003102447,0.0009371369,0.001001844,0.0002560765,0.000465825,0.003447642],"category_scores_gemma":[0.005583231,0.0001020452,0.0002668946,0.003151078,0.0002762469,0.001329003,0.0009204701,0.0004218515,0.0008615867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000637075,"about_ca_system_score_gemma":0.0002884018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008869993,"about_ca_topic_score_gemma":0.01171797,"domain_scores_codex":[0.9992404,0.0002865749,0.0000583686,0.0001156976,0.0001833507,0.0001156326],"domain_scores_gemma":[0.9952567,0.00264957,0.000866896,0.0002147805,0.0006530392,0.0003589687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001209494,0.0003815867,0.8530299,0.0006152383,0.0002547949,0.001261317,0.0386585,0.004320999,0.01293458,0.005387839,0.01723692,0.06470881],"study_design_scores_gemma":[0.00001304655,0.0001281675,0.940716,0.00005861756,0.00004224542,0.0002621163,0.02260106,0.01648765,0.001670934,0.001056211,0.01691981,0.00004419731],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899169,0.00007756596,0.0005213299,0.0002363999,0.00002088448,0.00006028786,0.004668968,0.00004226953,0.004455409],"genre_scores_gemma":[0.9911601,0.00009415704,0.001065047,0.00003421948,0.00003572111,0.0001526708,0.00547846,0.00002060748,0.00195912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008869993,"threshold_uncertainty_score":0.01763678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4516575332919505,"score_gpt":0.4743526640108456,"score_spread":0.02269513071889506,"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."}}