{"id":"W2123489600","doi":"10.5430/jbar.v2n2p61","title":"Analysis on Equity of China Medical Resources Allocation ------the Case of Shanghai","year":2013,"lang":"en","type":"article","venue":"Journal of Business Administration Research","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Equity (law); Lorenz curve; Gini coefficient; China; Population; Business; Government (linguistics); Investment (military); Public economics; Economics; Economic growth; Geography; Inequality; Medicine; Political science; Environmental health; Politics; Economic inequality","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":[0.006761063,0.0000775285,0.0004604693,0.0008210353,0.0001312858,0.00006532465,0.0003450726,0.0001416239,0.0006522146],"category_scores_gemma":[0.001325046,0.00004888443,0.0001532751,0.001691324,0.0001805285,0.0002189266,0.00005759111,0.0003604842,0.00002016226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007967556,"about_ca_system_score_gemma":0.0003221845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006392418,"about_ca_topic_score_gemma":0.0005822356,"domain_scores_codex":[0.9976584,0.0001396999,0.001477088,0.0001403238,0.0003838979,0.0002005435],"domain_scores_gemma":[0.997112,0.0001380801,0.001133084,0.0002898207,0.001174184,0.0001528005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001412051,0.005803421,0.287619,0.00508376,0.0045641,0.001625159,0.01343806,0.0042927,0.001204993,0.3044233,0.007117527,0.3634159],"study_design_scores_gemma":[0.0007099574,0.001122765,0.9788904,0.0002265755,0.00002628277,0.0004967226,0.001775797,0.006879518,0.0004639436,0.007580006,0.001667614,0.0001603706],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844375,0.0003618418,0.001190999,0.01109839,0.0001376631,0.0001967998,0.00001745668,0.000002024523,0.002557365],"genre_scores_gemma":[0.9993063,0.0001692894,0.00005605343,0.0000297201,0.0001983225,0.0000079393,0.000003856338,0.000007171506,0.0002213102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6912714,"threshold_uncertainty_score":0.9663467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1209290481180389,"score_gpt":0.3944034975885609,"score_spread":0.273474449470522,"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."}}