{"id":"W3123018117","doi":"","title":"Trade, migration and productivity: A quantitative analysis of China","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Productivity; Economics; General equilibrium theory; China; International economics; International trade; Labour economics; Macroeconomics; 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":[0.000697648,0.000476616,0.0004171859,0.003103927,0.0005818692,0.0008948433,0.0004706478,0.0003528952,0.002490264],"category_scores_gemma":[0.001155966,0.0001945765,0.0009597555,0.004470824,0.0006600545,0.000671392,0.0006545334,0.000286431,0.0001726914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003145142,"about_ca_system_score_gemma":0.001528228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1077228,"about_ca_topic_score_gemma":0.05511642,"domain_scores_codex":[0.9998068,0.00004738154,0.000009946819,0.00003435597,0.00003908047,0.00006251775],"domain_scores_gemma":[0.9992924,0.0002150046,0.000200631,0.00008028404,0.0001256409,0.00008599661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000249244,0.0001121662,0.7955288,0.0003602182,0.000474216,0.001541518,0.00163752,0.1430707,0.002470377,0.02108262,0.003504742,0.02996798],"study_design_scores_gemma":[0.00003211103,0.00008916032,0.8507123,0.00005555617,0.0001278181,0.0001530286,0.001068847,0.1370315,0.0006527446,0.005863863,0.004169116,0.00004385234],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947318,0.0004290928,0.0009866644,0.0002110843,0.000003544557,0.00001960808,0.001277167,0.00002680119,0.002314287],"genre_scores_gemma":[0.9973629,0.0002577515,0.0003011542,0.00001722118,0.000006709012,0.00001442425,0.0008408887,0.000005345831,0.001193718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1077228,"threshold_uncertainty_score":0.2141916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1097171673161899,"score_gpt":0.2592680147004539,"score_spread":0.1495508473842639,"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."}}