{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005316876,0.0001087907,0.0001286641,0.0007126776,0.002388767,0.002535756,0.0003231776,0.0005197825,0.005323409],"category_scores_gemma":[0.009489002,0.0001439352,0.0002303701,0.0008630431,0.002519814,0.00132975,0.001828616,0.000691244,0.0004052791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001547132,"about_ca_system_score_gemma":0.001447578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004069836,"about_ca_topic_score_gemma":0.00760261,"domain_scores_codex":[0.9958782,0.002554157,0.0001602276,0.0002800181,0.0003392967,0.0007879794],"domain_scores_gemma":[0.9948511,0.001959215,0.001758067,0.0004107779,0.0004783297,0.0005425527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008158024,0.0008706907,0.3951397,0.0001609799,0.00008300767,0.000992002,0.1057082,0.002000964,0.006249769,0.3636417,0.002941088,0.1213961],"study_design_scores_gemma":[0.0001214228,0.0007491543,0.6958208,0.000162344,0.00009104209,0.0004323447,0.1615001,0.008380203,0.005490722,0.06899952,0.05814332,0.0001090631],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9683651,0.00009047734,0.001955657,0.0006280411,0.0000166569,0.00003020288,0.00002014868,0.000005866318,0.02888787],"genre_scores_gemma":[0.9984837,0.00001910629,0.0001761431,0.000037117,0.000002936713,0.00000741844,0.000006800613,0.000001461996,0.001265324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005323409,"threshold_uncertainty_score":0.02811861,"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."}}