{"id":"W4398551297","doi":"10.7910/dvn/k0oyqf/alw63z","title":"polarization.R","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Social Media and Politics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Ideology; Word (group theory); Polarization (electrochemistry); Computer science; Natural language processing; Linguistics; Political science; Philosophy; Chemistry; Law; Politics","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003232995,0.002728933,0.002426795,0.005098541,0.001681329,0.006200481,0.003894541,0.001962193,0.3137115],"category_scores_gemma":[0.01808377,0.00126336,0.002068268,0.007087172,0.00106533,0.00292969,0.004352291,0.003082134,0.476855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001419444,"about_ca_system_score_gemma":0.003102374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008105687,"about_ca_topic_score_gemma":0.0150825,"domain_scores_codex":[0.9964644,0.0007540671,0.0003312589,0.001211736,0.0007786526,0.0004597918],"domain_scores_gemma":[0.9917347,0.00220737,0.0005059339,0.00365189,0.001343594,0.000556483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003921259,0.00001179593,0.0003576002,0.0003559935,0.00002632438,0.000008869677,0.00001373828,0.00008108882,0.00007138185,0.0008708959,0.9957837,0.002379544],"study_design_scores_gemma":[0.000178663,0.0000141418,0.001369627,0.0002418998,0.00003187283,0.0000411413,0.00003268672,0.0003104287,0.0003785965,0.00343317,0.9939429,0.00002486279],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001370898,0.0001067181,0.0003741457,0.0001863111,0.00008015114,0.00002618966,0.9909134,0.003217376,0.004958638],"genre_scores_gemma":[0.000877134,0.0001748908,0.001149057,0.0002477301,0.00004252296,0.0001704642,0.9917011,0.001789607,0.003847399],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6862885,"threshold_uncertainty_score":0.9789073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03221574396800937,"score_gpt":0.3109664220595581,"score_spread":0.2787506780915487,"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."}}