{"id":"W4401218838","doi":"10.2139/ssrn.4908563","title":"The Micro and Macro Productivity of Nations","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Macro; Productivity; Computer science; Economics; Macroeconomics; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009831842,0.0001787228,0.0001904012,0.001699585,0.0002573016,0.001829843,0.0001238318,0.0002884598,0.004912135],"category_scores_gemma":[0.005900538,0.0001159741,0.0001813695,0.002898932,0.0006228876,0.001976287,0.0007278041,0.0005736226,0.0007054332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006214518,"about_ca_system_score_gemma":0.0004649189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00447855,"about_ca_topic_score_gemma":0.004433047,"domain_scores_codex":[0.9996471,0.0001347316,0.00001918275,0.00004497176,0.00009592959,0.00005798327],"domain_scores_gemma":[0.9971437,0.001346578,0.0006860725,0.0001829025,0.0003530841,0.0002877243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001784536,0.00006808023,0.5792681,0.0001641022,0.0003188313,0.0004531647,0.002179175,0.01289484,0.0008595192,0.2937402,0.02135901,0.08851678],"study_design_scores_gemma":[0.00001566717,0.0001532338,0.7418396,0.0002376081,0.0001195262,0.0005757304,0.005252576,0.0124023,0.001030145,0.1643155,0.07401149,0.00004667772],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8796272,0.007687544,0.006442397,0.007094729,0.0003866944,0.00001433262,0.006128837,0.00008069171,0.09253754],"genre_scores_gemma":[0.9903278,0.002571962,0.0005731144,0.0001004481,0.0001362438,0.00001121276,0.000689889,0.00001070174,0.005578738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004912135,"threshold_uncertainty_score":0.01643276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01058648930028415,"score_gpt":0.2060152179403857,"score_spread":0.1954287286401016,"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."}}