{"id":"W2942872290","doi":"10.1002/asi.24316","title":"Female citation impact superiority 1996–2018 in six out of seven English‐speaking nations","year":2019,"lang":"en","type":"preprint","venue":"Journal of the Association for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Citation; Disadvantage; Promotion (chess); Citation impact; Inequality; Norm (philosophy); Psychological intervention; Demographic economics; Political science; Gender inequality; Demography; Psychology; Sociology; Economics; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","scholarly_communication"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.04603182,0.0001236205,0.0004553356,0.06336512,0.0002870102,0.001217127,0.002624238,0.000394891,0.000007878365],"category_scores_gemma":[0.2265326,0.00007778316,0.0002043287,0.06911848,0.0002708014,0.003631554,0.001097539,0.0006710077,0.000007254439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001346703,"about_ca_system_score_gemma":0.001881007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000432662,"about_ca_topic_score_gemma":0.00005780399,"domain_scores_codex":[0.99097,0.0001055042,0.001664811,0.0002158877,0.006695902,0.0003478698],"domain_scores_gemma":[0.9685482,0.002457491,0.004439574,0.0004665086,0.02400649,0.00008172954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002572599,0.00006507172,0.9544833,0.00004076793,0.0000385959,1.463599e-7,0.007318786,0.00271289,0.0004589091,0.003981396,0.001009915,0.02986446],"study_design_scores_gemma":[0.001697302,0.0004267217,0.9094021,0.0001695754,0.00003832616,0.000007734765,0.01020031,0.01871469,0.002850591,0.02909192,0.02709113,0.0003096409],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907792,0.0001018982,0.00120427,0.001946846,0.002890861,0.0005899668,0.0001190997,0.000009218445,0.002358674],"genre_scores_gemma":[0.9991095,0.0001118108,0.0006059801,0.00003394188,0.00004885628,0.000007240262,0.000002534699,0.000003089076,0.00007705578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1805008,"threshold_uncertainty_score":0.9998197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2316980430701267,"score_gpt":0.5053245853907983,"score_spread":0.2736265423206716,"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."}}