{"id":"W2975658760","doi":"10.1017/s1049096519001173","title":"How Many Citations to Women Is “Enough”? Estimates of Gender Representation in Political Science","year":2019,"lang":"en","type":"article","venue":"PS Political Science & Politics","topic":"Political Science Research and Education","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Politics; Representation (politics); Citation; Field (mathematics); Political science; Distribution (mathematics); Work (physics); Gender bias; Public relations; Social science; Sociology; Psychology; Social psychology; 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":[0.01552811,0.0002398752,0.0006747842,0.003749629,0.0009802128,0.002256348,0.0006172733,0.0008027412,0.004367393],"category_scores_gemma":[0.1448606,0.000175546,0.0003975825,0.005013849,0.001652476,0.002105979,0.002076555,0.000655847,0.0006703897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006395799,"about_ca_system_score_gemma":0.0005166361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002943539,"about_ca_topic_score_gemma":0.003104879,"domain_scores_codex":[0.9869898,0.007164817,0.001002333,0.001809107,0.002032869,0.001000993],"domain_scores_gemma":[0.8824744,0.07104357,0.02999247,0.006154297,0.007664261,0.002671048],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006565005,0.00007632381,0.9371336,0.0002932708,0.0003707767,0.0001130131,0.007010687,0.0004541768,0.001312271,0.01006687,0.004422869,0.03808958],"study_design_scores_gemma":[0.00003633706,0.0001415143,0.9624523,0.0002779847,0.0001899631,0.0002735291,0.004991774,0.001317329,0.001428946,0.0183916,0.01047205,0.00002661218],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729533,0.002892841,0.004831543,0.003549307,0.0001624397,0.00005147425,0.001688121,0.0000382538,0.01383263],"genre_scores_gemma":[0.9985123,0.0001705581,0.0003340129,0.0001833933,0.00007896734,0.00002529609,0.0002927933,0.000005656125,0.0003969181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9962504,"threshold_uncertainty_score":0.08212149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08081685276867762,"score_gpt":0.4536194545781833,"score_spread":0.3728026018095057,"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."}}