{"id":"W4398543691","doi":"10.7910/dvn/nfpjaq/w4iopx","title":"nodes_ALL.zip","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Gender Politics and Representation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Replication (statistics); Hierarchy; Sociology; History; Computer science; Genealogy; Political science; Biology","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":[0.0008557172,0.001857882,0.001221164,0.00478641,0.001310271,0.002834451,0.002172067,0.001973073,0.2076398],"category_scores_gemma":[0.005927253,0.0007924375,0.0009740795,0.00642108,0.0006380162,0.001992961,0.002788317,0.001851676,0.1638663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001741503,"about_ca_system_score_gemma":0.002542609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01560587,"about_ca_topic_score_gemma":0.0374369,"domain_scores_codex":[0.9992906,0.0001068838,0.00007638013,0.0002401697,0.0001569805,0.0001290247],"domain_scores_gemma":[0.9976006,0.0009148448,0.0002255663,0.0005483763,0.0004017363,0.000308819],"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":[0.00002530776,0.00001066665,0.0004284101,0.0004632745,0.00001017198,0.00001558593,0.00004232641,0.00009526868,0.00009690238,0.001064736,0.9960412,0.001706181],"study_design_scores_gemma":[0.0001048165,0.000006018152,0.001237068,0.0001635934,0.00001143898,0.00002831406,0.00008719599,0.0001561629,0.0002179818,0.001356924,0.9966171,0.00001341864],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009031572,0.00004884063,0.00005908528,0.00006903877,0.00002510823,0.00001011135,0.9981961,0.0002846146,0.001216843],"genre_scores_gemma":[0.0005970061,0.0001097797,0.0004211602,0.00008872912,0.00001505549,0.0001239204,0.9965492,0.0002136373,0.001881463],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7923602,"threshold_uncertainty_score":0.6946242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04931705013703705,"score_gpt":0.3295583066293233,"score_spread":0.2802412564922863,"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."}}