{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004800532,0.0007560858,0.0008753942,0.0003965861,0.001186457,0.0003233151,0.0008958765,0.0008547142,0.00005806889],"category_scores_gemma":[0.000148734,0.0005916476,0.000275934,0.0002788393,0.001947369,0.0001834808,0.0003142282,0.001005719,0.00274036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007257734,"about_ca_system_score_gemma":0.001625692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005772232,"about_ca_topic_score_gemma":0.0003782146,"domain_scores_codex":[0.9958777,0.0002533306,0.0008382637,0.00101646,0.001331828,0.0006824463],"domain_scores_gemma":[0.9974363,0.0002863502,0.0008346501,0.001004543,0.0001320599,0.0003061086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005746085,0.0005532162,0.07124454,0.001885639,0.003129532,0.0005082106,0.0008809364,0.00005886274,0.001121956,0.08921842,0.823554,0.007270053],"study_design_scores_gemma":[0.0008995528,0.00009527444,0.01701511,0.00107466,0.0003147325,0.0001807376,0.00005111866,0.00005313013,0.0001449441,0.0001258139,0.9792787,0.0007662137],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.008562542,0.01206626,0.00007356976,0.00007530168,0.003720296,0.001793361,0.0008039062,0.0006927507,0.972212],"genre_scores_gemma":[0.2257772,0.00007461504,0.0004409423,0.00006151113,0.007656859,0.0002203616,0.0003480912,0.00109139,0.7643291],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2172146,"threshold_uncertainty_score":0.9996535,"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."}}