{"id":"W4398357244","doi":"10.7910/dvn/k0oyqf/0imqrg","title":"table3.txt","year":2019,"lang":"it","type":"dataset","venue":"Harvard Dataverse","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Word (group theory); Ideology; Linguistics; Computer science; Natural language processing; Political science; Politics; Law; Philosophy","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":[],"category_scores_codex":[0.002275776,0.002535245,0.001771908,0.004978697,0.001530492,0.005041868,0.00328408,0.002142956,0.3873323],"category_scores_gemma":[0.01803584,0.0009111757,0.00144262,0.00833274,0.0007656493,0.002319989,0.003169897,0.002620399,0.3396295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001758712,"about_ca_system_score_gemma":0.003077958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01135881,"about_ca_topic_score_gemma":0.02238634,"domain_scores_codex":[0.9980747,0.0003374501,0.0003198343,0.0005881772,0.0003713207,0.0003085588],"domain_scores_gemma":[0.9920267,0.003468663,0.0006366872,0.001570542,0.001559192,0.0007380825],"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":[0.0000328564,0.00001218619,0.0002311497,0.0003775466,0.00001414773,0.000008159198,0.00001425856,0.00005725702,0.00004620125,0.0002723248,0.9978958,0.001037948],"study_design_scores_gemma":[0.0003370166,0.00002081284,0.001603099,0.0002910969,0.00002891183,0.00003696039,0.00009749233,0.000220336,0.0002958633,0.002267539,0.9947734,0.0000274566],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004258,0.0000194938,0.00005208892,0.00006896607,0.00002707003,0.00001290831,0.9987239,0.0003676584,0.0006852208],"genre_scores_gemma":[0.0004053361,0.00004894435,0.0003699956,0.0001358468,0.00002223372,0.0002017924,0.996838,0.0003646142,0.001613222],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6126677,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04743174483743739,"score_gpt":0.3381377997863732,"score_spread":0.2907060549489358,"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."}}