{"id":"W2052399702","doi":"10.5539/ies.v3n1p23","title":"Enrollment Quota Control, Elite Selection and Access to Education in Rural China","year":2010,"lang":"en","type":"article","venue":"International Education Studies","topic":"Intergenerational and Educational Inequality Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Disadvantaged; Multivariate probit model; Selection (genetic algorithm); Economic growth; Rural area; Control (management); China; Demographic economics; Elite; Economics; Political science; Politics","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":[0.001221686,0.0001469366,0.0002887253,0.0006773188,0.0006441296,0.0009523468,0.0003901749,0.0002327982,0.0028002],"category_scores_gemma":[0.002900874,0.00009834986,0.0002172524,0.001063885,0.001183966,0.0007171694,0.001076879,0.000311518,0.00009419978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001762402,"about_ca_system_score_gemma":0.001359082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04622253,"about_ca_topic_score_gemma":0.0443575,"domain_scores_codex":[0.9992294,0.0002018733,0.00004111581,0.00009341363,0.0001118807,0.0003223362],"domain_scores_gemma":[0.9978068,0.0005325382,0.0008926808,0.0001434582,0.0001524519,0.0004719285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003127726,0.0004093246,0.9256641,0.00005464663,0.00009144207,0.000548234,0.002622782,0.006311363,0.001098085,0.04014266,0.0007318234,0.0220128],"study_design_scores_gemma":[0.0000734855,0.0002384388,0.958386,0.00003029907,0.00004408287,0.00008076816,0.002358391,0.02110807,0.0003983613,0.01464813,0.00261415,0.00001984183],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967543,0.00008130523,0.0003881979,0.0002248835,0.000002664076,0.000008177518,0.00004833655,0.000005148778,0.00248698],"genre_scores_gemma":[0.9996969,0.00001959125,0.00002146796,0.000009832816,0.000002485604,0.000002078048,0.00001545324,4.890842e-7,0.0002317971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04622253,"threshold_uncertainty_score":0.09190696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04506477056174409,"score_gpt":0.4649836080850483,"score_spread":0.4199188375233043,"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."}}