{"id":"W2886105060","doi":"10.1017/pan.2018.12","title":"Gendered Citation Patterns across Political Science and Social Science Methodology Fields","year":2018,"lang":"en","type":"article","venue":"Political Analysis","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":550,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"University of Cambridge","keywords":"Citation; Gender gap; Politics; Publication; Field (mathematics); Sociology; Social science; Gender studies; Political science; Demographic economics; Law","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01147343,0.0002920926,0.0007236687,0.02032523,0.001095744,0.003060489,0.0008096595,0.0008195349,0.005650449],"category_scores_gemma":[0.08999514,0.0002519291,0.0006383446,0.022967,0.001282634,0.00330451,0.002777017,0.0004759262,0.001450208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271444,"about_ca_system_score_gemma":0.001457159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002861282,"about_ca_topic_score_gemma":0.005239066,"domain_scores_codex":[0.9889402,0.002550575,0.001825946,0.001634529,0.004075991,0.0009728789],"domain_scores_gemma":[0.8731368,0.06282201,0.02639859,0.00653233,0.0273387,0.003771439],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000488147,0.00007666546,0.8361839,0.001559638,0.0003745286,0.000454167,0.02265988,0.0004772781,0.003896668,0.01536172,0.006246523,0.1122208],"study_design_scores_gemma":[0.00002558732,0.0001492966,0.9480225,0.0006818757,0.0001259673,0.0007533218,0.01022582,0.0008768731,0.002156241,0.01105881,0.0258733,0.00005036248],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9526095,0.01513986,0.002838696,0.002344747,0.000222157,0.00008394539,0.004828881,0.0001079739,0.0218243],"genre_scores_gemma":[0.9881566,0.003025597,0.001421092,0.0002392962,0.0002084109,0.00007443689,0.002592058,0.00006391247,0.004218696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9885266,"threshold_uncertainty_score":0.06067801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7631996924929744,"score_gpt":0.6632523770784542,"score_spread":0.09994731541452018,"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."}}