{"id":"W7132271990","doi":"","title":"The Middle-Income Trap (MIT): A Provincial Comparison between Shaanxi and Jiangsu","year":2019,"lang":"en","type":"other","venue":"CEIBS Institutional Repository","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Casa","funders":"","keywords":"China; Per capita; Poverty trap; Poverty; Middle income trap; Trap (plumbing)","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":[0.0006826968,0.0001457017,0.0001935595,0.001489522,0.001470484,0.0008927244,0.0003856696,0.0001889242,0.00203539],"category_scores_gemma":[0.001078995,0.00009759054,0.000279121,0.003376004,0.0007105478,0.0004974951,0.001142256,0.0003753008,0.0001476843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005028969,"about_ca_system_score_gemma":0.00647256,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6292821,"about_ca_topic_score_gemma":0.773307,"domain_scores_codex":[0.9995771,0.00005305332,0.00002157029,0.00003853914,0.0000987939,0.0002109548],"domain_scores_gemma":[0.9988298,0.00008954322,0.0002486726,0.00004031412,0.0005022859,0.0002895636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002341395,0.00005541125,0.9730203,0.0000817434,0.00005357567,0.0003927281,0.005561381,0.0003079635,0.0003870028,0.003153995,0.002035258,0.01471654],"study_design_scores_gemma":[0.000007741779,0.00003789133,0.9905709,0.00002808052,0.00001978053,0.00005659862,0.006568358,0.0004965022,0.0000714145,0.0001916872,0.001943453,0.000007531779],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951335,0.0003158603,0.00006825181,0.0004096402,0.000009606199,0.000008210711,0.0003545127,0.000005180416,0.003695206],"genre_scores_gemma":[0.9986701,0.0001306863,0.00004874495,0.00004735701,0.000003338705,0.000004449533,0.0004746345,0.000002332913,0.0006184671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6292821,"threshold_uncertainty_score":0.7458022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02752408267273726,"score_gpt":0.2628748303578907,"score_spread":0.2353507476851534,"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."}}