{"id":"W4404344228","doi":"10.48550/arxiv.2411.00008","title":"Women in Science: Measuring Participation in Europe Across Disciplines, Generations and Over Time","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Economic and Technological Developments in Russia","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Data science; Computer science; Sociology; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009007667,0.0002409073,0.000523237,0.007175451,0.0009037937,0.003056308,0.0006390355,0.0006556817,0.002317764],"category_scores_gemma":[0.02412505,0.0001447827,0.0005012667,0.01374903,0.001347993,0.003314769,0.003812869,0.0004567171,0.0004441322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008817847,"about_ca_system_score_gemma":0.001039201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00575278,"about_ca_topic_score_gemma":0.00559224,"domain_scores_codex":[0.9957399,0.001351245,0.0005491919,0.0009080422,0.0009932241,0.0004583505],"domain_scores_gemma":[0.983232,0.006367324,0.006040757,0.00109896,0.001905528,0.001355416],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001191246,0.00002756293,0.9545307,0.0001829994,0.0001939405,0.00009492254,0.0129789,0.0002334532,0.0003578269,0.002237111,0.00123501,0.02780838],"study_design_scores_gemma":[0.00000739461,0.00007141568,0.9599581,0.0001296776,0.00008033356,0.0002723858,0.01543853,0.0002869214,0.0004410604,0.001769946,0.02151812,0.0000261053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983488,0.003029739,0.001127209,0.001072622,0.0000704936,0.00002340107,0.001806311,0.00001752027,0.009364788],"genre_scores_gemma":[0.9950917,0.001129369,0.0006450849,0.000301611,0.00007737462,0.00004327328,0.0009568656,0.000013512,0.001741329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9909923,"threshold_uncertainty_score":0.04763764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1048938210129511,"score_gpt":0.264056665021333,"score_spread":0.1591628440083819,"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."}}