{"id":"W3164579996","doi":"10.1080/2157930x.2021.1932062","title":"Catching-up national innovations systems (NIS) in China and post-catching-up NIS in Korea and Taiwan: verifying the detour hypothesis and policy implications","year":2021,"lang":"en","type":"article","venue":"Innovation and Development","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Academy of Korean Studies","keywords":"China; National innovation system; National Policy; Political science; Economic growth; Regional science; Development economics; Economics; Business; International trade; Economy; 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":[0.00101544,0.0001478634,0.0002434314,0.0006639207,0.0002972743,0.0001985876,0.00006750399,0.00008256525,0.000008145014],"category_scores_gemma":[0.0005026386,0.0001550156,0.000008761951,0.000955371,0.0000684466,0.0003068396,0.0001041923,0.0001841119,0.000004596919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001547983,"about_ca_system_score_gemma":0.0002966184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005934412,"about_ca_topic_score_gemma":0.0003816584,"domain_scores_codex":[0.9986104,0.00002761687,0.0007023304,0.0004248628,0.00004235001,0.0001924426],"domain_scores_gemma":[0.9993548,0.0001009536,0.0002379786,0.0001364875,0.0001310058,0.00003871043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00001034352,0.00003873368,0.4595892,0.00006401958,0.00002818047,6.174901e-7,0.004552761,0.00001612585,0.0002506456,0.5168234,0.0001039843,0.01852203],"study_design_scores_gemma":[0.0005271329,0.000007826199,0.9613889,0.00002266826,0.000001281259,0.00003310672,0.0005364122,0.0002519088,0.00007097227,0.02993177,0.007037843,0.0001901367],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982585,0.000958902,0.0004914318,0.01395266,0.0001561956,0.0002395842,0.00006616094,0.00001332065,0.001536716],"genre_scores_gemma":[0.997479,0.0001987556,0.0009521723,0.0008899051,0.0000599431,0.00008178358,0.00005876115,0.00001269944,0.0002670207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5017998,"threshold_uncertainty_score":0.6321353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05081702248495511,"score_gpt":0.2511039191609402,"score_spread":0.2002868966759851,"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."}}