{"id":"W2388673644","doi":"","title":"Cross-country Comparison and Reference of R&D Tax Incentives","year":2014,"lang":"en","type":"article","venue":"Ke-ji guanli yanjiu","topic":"Innovation Policy and R&D","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Incentive; China; Tax policy; Cross country; Business; Public economics; International economics; Economics; Tax reform; Political science; Market economy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00237322,0.0002858314,0.0005443502,0.004373082,0.0005429889,0.00182109,0.0003628553,0.0003665279,0.002433492],"category_scores_gemma":[0.006548782,0.0001731019,0.0008517255,0.005989927,0.0003649392,0.00109682,0.0008813223,0.0007520568,0.0004516892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001108892,"about_ca_system_score_gemma":0.0006801141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01000303,"about_ca_topic_score_gemma":0.009914746,"domain_scores_codex":[0.9980665,0.0005552784,0.0001715144,0.0004172896,0.0004147559,0.0003745959],"domain_scores_gemma":[0.9930121,0.001907245,0.001905547,0.0009309421,0.001933981,0.0003101974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009895635,0.0002880127,0.7607104,0.0006315601,0.002161512,0.00166453,0.001481461,0.01972919,0.003429957,0.07667017,0.01380691,0.1184367],"study_design_scores_gemma":[0.00004061222,0.0002524542,0.939493,0.0001920189,0.0009246384,0.0007225072,0.002434268,0.005695851,0.007076554,0.0047827,0.03829607,0.00008936398],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9043405,0.008464501,0.01024654,0.001235999,0.0003442634,0.00009340897,0.007729219,0.0001919084,0.06735367],"genre_scores_gemma":[0.9885995,0.001545988,0.001763331,0.0001617028,0.0000401527,0.00002492815,0.004697345,0.00003239025,0.003134602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01000303,"threshold_uncertainty_score":0.01988959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04804813973025021,"score_gpt":0.2904928788768438,"score_spread":0.2424447391465936,"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."}}