{"id":"W3176264954","doi":"10.1080/09537325.2021.1947487","title":"Global value chain embeddedness and innovation efficiency in China","year":2021,"lang":"en","type":"article","venue":"Technology Analysis and Strategic Management","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Global value chain; Embeddedness; Value (mathematics); China; Business; Position (finance); Value chain; Affect (linguistics); Chain (unit); Human capital; Stochastic frontier analysis; Industrial organization; Frontier; Economic geography; Economics; Supply chain; Microeconomics; Economic growth; Marketing; International trade; Comparative advantage; Production (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":[0.00108758,0.0002722449,0.0002753321,0.00191375,0.0003640531,0.001532582,0.0002404793,0.0002123507,0.0014657],"category_scores_gemma":[0.001926086,0.0001248843,0.0005094873,0.002746103,0.00070883,0.001167568,0.0009466579,0.0002601522,0.0001128002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002024978,"about_ca_system_score_gemma":0.002213867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03563409,"about_ca_topic_score_gemma":0.0385091,"domain_scores_codex":[0.999587,0.00005960591,0.00003470395,0.00007312129,0.0001054376,0.0001401396],"domain_scores_gemma":[0.9985077,0.0003622599,0.0005127541,0.0001106779,0.0003210692,0.0001855295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006342903,0.00006328534,0.9357876,0.0001143956,0.0002061374,0.0003896823,0.001050975,0.0221935,0.001733217,0.01005281,0.0003978407,0.02794722],"study_design_scores_gemma":[0.00001223287,0.00006874138,0.9754985,0.00003521147,0.00008083448,0.00006390327,0.0007265126,0.01706216,0.0008036265,0.00324443,0.002384207,0.00001948191],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963382,0.0001943139,0.000628806,0.00008572211,0.00000197517,0.000005946657,0.0001127164,0.000008290312,0.002623874],"genre_scores_gemma":[0.9993615,0.00009048319,0.0001034263,0.000005604494,0.00000155993,0.00000202266,0.00009802356,0.000001358178,0.0003360305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03563409,"threshold_uncertainty_score":0.07085335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02688827886180625,"score_gpt":0.2301878723867921,"score_spread":0.2032995935249858,"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."}}