{"id":"W3124223169","doi":"10.2139/ssrn.2468801","title":"Political Selection in China: The Complementary Roles of Connections and Performance","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"China's Socioeconomic Reforms and Governance","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Politics; Selection (genetic algorithm); China; Political science; Computer 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":[0.001290314,0.0002019466,0.0004517987,0.002245608,0.002487121,0.002409258,0.0003591426,0.0004505968,0.005691949],"category_scores_gemma":[0.002024352,0.0001168335,0.0001924074,0.002884575,0.002478783,0.0008245143,0.001512043,0.0004026522,0.0002668794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003385591,"about_ca_system_score_gemma":0.003733864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04006351,"about_ca_topic_score_gemma":0.08715416,"domain_scores_codex":[0.9987749,0.000294209,0.00004680136,0.0001436118,0.0001980467,0.0005424792],"domain_scores_gemma":[0.998301,0.0002810638,0.0003997873,0.00009751603,0.0002789542,0.0006417814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002672698,0.0001413972,0.8668851,0.00006966201,0.0001215081,0.0004711048,0.007899437,0.002138476,0.001268699,0.0721934,0.002288888,0.04625497],"study_design_scores_gemma":[0.00002232718,0.00006035816,0.9834666,0.00001175562,0.00003107354,0.00002507458,0.002768085,0.001718202,0.000185755,0.007386452,0.004308169,0.00001615614],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890381,0.0001441382,0.0001252351,0.0007693066,0.000007979004,0.0000121637,0.00006144145,0.000006432089,0.0098352],"genre_scores_gemma":[0.9990197,0.00003415636,0.0000111318,0.00002624172,0.000006357938,0.000002152813,0.00001802673,0.000001015537,0.0008812088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04006351,"threshold_uncertainty_score":0.07966059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005858321661227798,"score_gpt":0.2544105153159958,"score_spread":0.248552193654768,"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."}}