{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002309227,0.0005597273,0.0009367091,0.0003713789,0.0007424256,0.0004951074,0.001845991,0.0005557839,0.4128049],"category_scores_gemma":[0.000855757,0.000561346,0.0005140784,0.001134329,0.0004104976,0.0006689598,0.000890489,0.0007064978,0.8231879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002506188,"about_ca_system_score_gemma":0.0008366712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004424022,"about_ca_topic_score_gemma":0.0006031267,"domain_scores_codex":[0.9947038,0.001115342,0.0007362841,0.001119039,0.001513834,0.0008116439],"domain_scores_gemma":[0.996158,0.001008826,0.000614441,0.001522916,0.000303545,0.0003922504],"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":[0.00003468362,0.000172873,0.00005437076,0.0001030686,0.0003382541,0.00005120471,0.0003676917,0.000181828,0.000005567751,0.001790216,0.9945773,0.002322959],"study_design_scores_gemma":[0.0004075193,0.00005116216,0.0001247432,0.000106802,0.0006746686,0.000003784181,0.0009451298,0.0006675636,0.000002311371,0.0005639723,0.995773,0.0006793728],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003756235,0.000002785694,0.0009855705,0.00004670367,0.003089041,0.0004482886,0.9852824,0.00004931742,0.01005838],"genre_scores_gemma":[0.00008507777,0.00105028,0.002044244,0.0007576896,0.001578656,0.0000157004,0.96317,0.00003161169,0.03126672],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.410383,"threshold_uncertainty_score":0.9996838,"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."}}