{"id":"W2049205345","doi":"10.1016/j.iref.2014.08.004","title":"The growth and inequality nexus: The case of China","year":2014,"lang":"en","type":"article","venue":"International Review of Economics & Finance","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Nexus (standard); Inequality; Economics; Economic inequality; China; Income inequality metrics; Government (linguistics); Development economics; Geography","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":[0.000508661,0.0003340995,0.000607925,0.001503229,0.001951803,0.002537522,0.0007626368,0.001128156,0.002037859],"category_scores_gemma":[0.0008279624,0.0001348261,0.0005029505,0.003675302,0.002838253,0.002068577,0.002093706,0.001189028,0.00009654772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004932377,"about_ca_system_score_gemma":0.004488819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2391592,"about_ca_topic_score_gemma":0.2739978,"domain_scores_codex":[0.9997222,0.00005066296,0.000005910604,0.00002277734,0.0000389702,0.0001593832],"domain_scores_gemma":[0.9995341,0.0001214163,0.00009884356,0.00002407028,0.00007999818,0.0001416078],"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":[0.000253683,0.0001875922,0.1729889,0.0002522122,0.0001817649,0.005171527,0.005977315,0.02177106,0.0008034145,0.7376208,0.00918592,0.04560585],"study_design_scores_gemma":[0.0001249358,0.0001614537,0.4078771,0.0003610386,0.0002977866,0.0008138692,0.01626295,0.08853385,0.0006726005,0.4509058,0.03386048,0.000128182],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9449344,0.007264726,0.001085873,0.01266384,0.00004101447,0.00001882106,0.000235216,0.00001734191,0.03373877],"genre_scores_gemma":[0.9965628,0.001839844,0.0001265989,0.000135975,0.00002928886,0.000005224229,0.00003737691,0.000002431882,0.001260455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2391592,"threshold_uncertainty_score":0.4755342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02744055678546106,"score_gpt":0.3215477057525685,"score_spread":0.2941071489671075,"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."}}