{"id":"W1990342896","doi":"10.1007/s10961-012-9259-6","title":"Productivity growth, trade and FDI nexus: evidence from the Canadian manufacturing sector","year":2012,"lang":"en","type":"article","venue":"The Journal of Technology Transfer","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Innovation, Science and Economic Development Canada; Government of Canada","funders":"","keywords":"Lagging; Nexus (standard); Foreign direct investment; Technology transfer; Productivity; International trade; Economics; International economics; Panel data; Total factor productivity; Economic geography; Macroeconomics; Econometrics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009886656,0.0004681509,0.0004183835,0.004097603,0.002269581,0.00225131,0.0009232312,0.0006277239,0.0037906],"category_scores_gemma":[0.005487018,0.0002106538,0.0006511271,0.01310044,0.001198878,0.0009447411,0.001057906,0.0008520412,0.0003726189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02224488,"about_ca_system_score_gemma":0.02952466,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9935275,"about_ca_topic_score_gemma":0.9968678,"domain_scores_codex":[0.9991856,0.00004662179,0.00003395131,0.00007736541,0.0003555827,0.0003008902],"domain_scores_gemma":[0.9939519,0.001315401,0.001216751,0.0001887508,0.002698631,0.0006285215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003408055,0.0001566078,0.9402634,0.0002823673,0.0002928002,0.0005655168,0.002286473,0.002808705,0.0005503671,0.005017287,0.01066899,0.0367666],"study_design_scores_gemma":[0.0000148785,0.00001665213,0.9919546,0.00005470737,0.000114766,0.0000339196,0.001963801,0.0007768065,0.000199652,0.0003212768,0.004527234,0.00002169674],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973027,0.005788829,0.0001622547,0.002669784,0.00002319222,0.00002236142,0.006128565,0.00002286286,0.01215507],"genre_scores_gemma":[0.9892303,0.004744104,0.0001204167,0.0001464561,0.00002098041,0.000005653796,0.002950673,0.000006010972,0.002775228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02224488,"threshold_uncertainty_score":0.1613986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03756876243550231,"score_gpt":0.2004868404053812,"score_spread":0.1629180779698789,"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."}}