{"id":"W1489097268","doi":"","title":"The Productivity Differential Between the Canadian and U.S. Manufacturing Sectors: A Perspective Drawn from the Early 20th Century","year":2008,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Productivity; Total factor productivity; Economics; Labour economics; Differential (mechanical device); Capital (architecture); Production (economics); Demographic economics; International comparisons; Multifactor productivity; Capital intensity; Manufacturing; Human capital; Agricultural economics; Business; Economic growth; Geography; Engineering; Macroeconomics","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006026335,0.0002327786,0.0002762126,0.0001494119,0.00847854,0.00149518,0.001670937,0.00008371935,0.00003765197],"category_scores_gemma":[0.001267531,0.00009599065,0.0002213515,0.0004841667,0.0009558599,0.0003049143,0.0001393724,0.002806119,0.00003476279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348015,"about_ca_system_score_gemma":0.003884723,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1597355,"about_ca_topic_score_gemma":0.6807101,"domain_scores_codex":[0.9945462,0.0009575786,0.0004556391,0.0005308371,0.001544171,0.001965577],"domain_scores_gemma":[0.9966065,0.001847698,0.0003567462,0.0007459773,0.0002613936,0.0001816652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002501763,0.0001689748,0.7559105,0.000001376746,0.002733426,0.00005537891,0.05807872,0.0003716131,0.0003342026,0.03953123,0.003067252,0.1394971],"study_design_scores_gemma":[0.0002786244,0.0001021447,0.8015231,0.000007709566,0.0001241324,0.0001962345,0.007845243,0.0001030695,0.0001780761,0.1803347,0.009085387,0.0002215377],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9766788,0.003643951,0.0007305884,0.01818508,0.0003248819,0.0001962688,0.00001293217,0.00001362281,0.0002138778],"genre_scores_gemma":[0.9974075,0.0006246411,0.00000585859,0.00008029219,0.001221508,0.000003405934,9.692869e-7,0.00001648348,0.0006393617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5209746,"threshold_uncertainty_score":0.9995413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464186976405111,"score_gpt":0.2823108956877596,"score_spread":0.2576690259237086,"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."}}