{"id":"W2474556973","doi":"10.1057/eps.2015.98","title":"fuzzy sets … too fuzzy to study women’s representation in parliament!","year":2016,"lang":"en","type":"article","venue":"European Political Science","topic":"Gender Politics and Representation","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Parliament; Representation (politics); Rebuttal; Fuzzy logic; Set (abstract data type); Fuzzy set; Variation (astronomy); Computer science; Politics; Political science; Law; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.02377883,0.0005675677,0.0009945452,0.002450139,0.003672678,0.005176686,0.002427225,0.006169951,0.004977598],"category_scores_gemma":[0.08357277,0.0003532609,0.001468707,0.001936495,0.01788715,0.01463934,0.00316459,0.0124693,0.001448275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003583022,"about_ca_system_score_gemma":0.002155498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005567226,"about_ca_topic_score_gemma":0.005125025,"domain_scores_codex":[0.9849833,0.009214076,0.0005691513,0.001229918,0.003564628,0.0004389889],"domain_scores_gemma":[0.9280565,0.05886029,0.001988098,0.002911485,0.007438972,0.0007446368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000629667,0.00003075945,0.001260191,0.0003872544,0.00009667945,0.00004951933,0.009775246,0.0003031584,0.000109876,0.6690248,0.2714602,0.04743924],"study_design_scores_gemma":[0.00003754705,0.00006108237,0.001792711,0.001448924,0.00004693309,0.0001075981,0.01026609,0.0007368295,0.0002666821,0.6512972,0.3338751,0.0000632476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002800278,0.01420318,0.006865001,0.9498668,0.01243571,0.00001831717,0.000111063,0.00002609384,0.01367365],"genre_scores_gemma":[0.3184378,0.02217661,0.01908871,0.5707721,0.04652085,0.0004999146,0.0001720186,0.0001807774,0.02215115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02377883,"threshold_uncertainty_score":0.125756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0540433536920998,"score_gpt":0.3707128683321598,"score_spread":0.31666951464006,"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."}}