{"id":"W2021266269","doi":"10.1016/j.chieco.2014.04.008","title":"Challenges of working with the Chinese NBS firm-level data","year":2014,"lang":"en","type":"article","venue":"China Economic Review","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":302,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Comparability; Productivity; China; Tracing; Econometrics; Point (geometry); Panel data; Politics; Industrial organization; Economics; Business; Computer science; Macroeconomics; Political science; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001679586,0.000261083,0.0009784043,0.00005873521,0.00009014606,0.00004472644,0.001420426,0.00006605629,0.0002627668],"category_scores_gemma":[0.0000879502,0.0001913802,0.0001420787,0.00009666654,0.0001032072,0.0003232116,0.0002610826,0.0001726224,0.000843345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008283775,"about_ca_system_score_gemma":0.00002304795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001029532,"about_ca_topic_score_gemma":0.0002089167,"domain_scores_codex":[0.9981395,0.00003404373,0.0008689646,0.0006347757,0.00002090211,0.0003018227],"domain_scores_gemma":[0.9970891,0.0001097571,0.0008038182,0.001909373,0.000007743931,0.00008017269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002792405,0.000108413,0.05419134,0.002383841,0.000397122,0.000001579076,0.0003127561,0.0003124936,6.630645e-7,0.830828,0.01406763,0.09736823],"study_design_scores_gemma":[0.0005965588,0.00008030125,0.1317693,0.000806728,0.00004222338,0.00002612318,0.00002442298,0.002533312,0.000001639209,0.01355466,0.8500875,0.0004773083],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1164803,0.6008391,0.0005968898,0.02090039,0.0007975901,0.00093751,0.0004142908,0.00005756364,0.2589763],"genre_scores_gemma":[0.8169127,0.181395,0.0004196114,0.0008205847,0.0002291566,0.00002330005,0.00004623398,0.00003339501,0.0001200494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8360198,"threshold_uncertainty_score":0.9999346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.254982536079309,"score_gpt":0.2672760018700688,"score_spread":0.01229346579075974,"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."}}