{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003363338,0.000121805,0.000370081,0.001020935,0.00006836573,0.00006385917,0.0001123318,0.0001303711,0.00009428271],"category_scores_gemma":[0.000007382007,0.0001443638,0.00004206411,0.004352953,0.00007567064,0.00006263256,0.0001078824,0.00008545187,0.00001552153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000562247,"about_ca_system_score_gemma":0.000006522397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000110823,"about_ca_topic_score_gemma":0.0002150189,"domain_scores_codex":[0.9988092,0.000008572619,0.0004665763,0.0004751445,0.00001971965,0.00022075],"domain_scores_gemma":[0.9995735,0.000003901332,0.0001404336,0.0002455344,0.000012907,0.00002369839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002179461,0.00005249173,0.1627086,0.00001904346,0.0002009644,0.00002499938,0.00001805827,0.0003730787,9.569233e-7,0.8358236,0.000005238393,0.0007707828],"study_design_scores_gemma":[0.0004345913,0.0000262985,0.1938737,0.000006288745,0.00007415,0.000006270777,0.0009954228,0.01802313,0.000007480229,0.7859774,0.0003822875,0.0001930478],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8891038,0.001383238,0.00198136,0.001109756,0.000045959,0.00007877801,0.00001810619,0.00003132454,0.1062477],"genre_scores_gemma":[0.9981536,0.0006592595,0.0006738915,0.0001027199,0.000006341569,0.00001250111,0.0000211523,0.000003347334,0.0003671358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1090499,"threshold_uncertainty_score":0.5886985,"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."}}