{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006469944,0.0001439158,0.0001689285,0.0003567359,0.0004769881,0.0002209909,0.0002486296,0.00005904352,0.000165243],"category_scores_gemma":[0.001774358,0.0001418644,0.00004097022,0.000304466,0.0001181223,0.0006257154,0.00007737329,0.0001715876,0.00004188718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000323714,"about_ca_system_score_gemma":0.001145753,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005498716,"about_ca_topic_score_gemma":0.02342174,"domain_scores_codex":[0.9986323,0.0001022465,0.0003666086,0.0002686639,0.0004175282,0.0002126643],"domain_scores_gemma":[0.9977483,0.0001655637,0.0001190575,0.00008354774,0.00176934,0.0001141865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001151598,0.001181206,0.3608117,0.00002818398,0.0002513596,1.608284e-7,0.03523456,0.0001099639,0.001949842,0.3958981,0.1003543,0.1040655],"study_design_scores_gemma":[0.0002846424,0.00002725105,0.6022654,0.00009010315,0.00001885714,0.000003939627,0.02012416,0.00005280142,0.0001115671,0.02358935,0.353147,0.0002849253],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9023288,0.0005404866,0.00009502694,0.06839506,0.01222446,0.0003922842,0.00001126705,0.00003606626,0.01597653],"genre_scores_gemma":[0.9847418,0.0005609404,0.001004468,0.003628696,0.002038883,0.0005014154,0.00002777379,0.000008920641,0.007487081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3723088,"threshold_uncertainty_score":0.9943982,"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."}}