{"id":"W2515387558","doi":"10.5509/2016892345","title":"Gender Quotas and Candidate Selection Processes in South Korean Political Parties","year":2016,"lang":"en","type":"article","venue":"Pacific Affairs","topic":"Gender Politics and Representation","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Politics; Selection (genetic algorithm); Political science; Political economy; Sociology; Computer science; Law; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002122374,0.00006884359,0.00008091515,0.00007153291,0.000174771,0.00005191481,0.00004618975,0.00006019962,0.00005598401],"category_scores_gemma":[0.0001725659,0.00004840088,0.00001498776,0.0001538541,0.0002507218,0.0001317657,0.00001511842,0.00004214793,0.00001941376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007283521,"about_ca_system_score_gemma":0.000198612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009551778,"about_ca_topic_score_gemma":0.002077684,"domain_scores_codex":[0.999029,0.00009155941,0.0001186945,0.0001855755,0.0001815364,0.0003936666],"domain_scores_gemma":[0.9996486,0.00006262604,0.00003124452,0.00005758419,0.00005905666,0.0001409388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002308999,0.00006279679,0.4096257,0.00007035275,0.00002248902,0.000004895273,0.199317,0.000002528717,0.0003706127,0.388721,0.000356224,0.00142329],"study_design_scores_gemma":[0.0004338673,0.00003152312,0.01638828,0.00002895982,0.0000150876,0.00000317185,0.9369766,0.00002285876,0.001433453,0.04229682,0.002146632,0.0002227705],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.324104,0.0001951937,0.0006203039,0.003128428,0.000270205,0.000316938,0.00002670575,0.0001191306,0.6712191],"genre_scores_gemma":[0.9985702,0.00004350876,0.00004236307,0.000006711927,0.0001320909,0.00001474213,0.000001764162,0.000006804089,0.001181803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7376596,"threshold_uncertainty_score":0.197373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0296788246153791,"score_gpt":0.2901907668233678,"score_spread":0.2605119422079886,"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."}}