{"id":"W3187020100","doi":"10.3390/su13158188","title":"The Effect of Government Subsidy Policy for Foreign Capital R&D from the Perspective of Global Supply Chains: An Empirical Analysis with Time Lag and Propensity Score Matching","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Innovation Policy and R&D","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Ministry of Education","keywords":"Subsidy; Propensity score matching; Panel data; Matching (statistics); Government (linguistics); Economics; Supply chain; Business; Industrial organization; Public economics; Microeconomics; Econometrics; Market economy; Marketing","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":[0.001173549,0.0001272053,0.0004376845,0.00002592068,0.0002307398,0.00004863404,0.0001571663,0.00006153,0.00001633972],"category_scores_gemma":[0.001255462,0.00007776869,0.0001392796,0.0006645861,0.0002966675,0.0001141676,0.00009456801,0.00008969992,6.285193e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008111866,"about_ca_system_score_gemma":0.0001815714,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007303197,"about_ca_topic_score_gemma":0.002051896,"domain_scores_codex":[0.9988589,0.0001139469,0.0003742444,0.0003479438,0.00008389625,0.0002210206],"domain_scores_gemma":[0.9984598,0.0003700315,0.0002874864,0.0004796968,0.0003639559,0.00003909545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002602295,0.0000547094,0.7027356,0.0000389279,0.000270699,5.87967e-7,0.001682518,0.00006955965,0.000003886565,0.2947176,0.00002211143,0.0001435242],"study_design_scores_gemma":[0.0004709722,0.0002538254,0.7935897,0.000003588416,0.00006705718,0.000001123597,0.002890175,0.0002829503,0.0002082786,0.2020502,0.00009240776,0.00008977639],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920619,0.0002143425,0.001296134,0.003972412,0.00001247435,0.0006214112,0.001275123,0.000006393616,0.0005398582],"genre_scores_gemma":[0.9995587,0.000005366969,0.0001096144,0.00009033368,0.00005466713,0.00003106653,0.00002963281,0.000006892759,0.0001136768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0926675,"threshold_uncertainty_score":0.9993073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01814522298929565,"score_gpt":0.2756983231465951,"score_spread":0.2575531001572995,"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."}}