{"id":"W1532105698","doi":"","title":"International Productivity Comparisons: An Examination of Data Sources","year":2003,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Productivity; Data science; Computer science; Economics; Macroeconomics","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":[0.006785297,0.0001492978,0.0004266645,0.0005324163,0.000101664,0.00007566711,0.0009283879,0.0001161812,0.0003017377],"category_scores_gemma":[0.00128264,0.0001942531,0.00004968718,0.000185038,0.0002569625,0.001213702,0.0002346304,0.000372709,0.00003430464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002403183,"about_ca_system_score_gemma":0.00008402357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008488722,"about_ca_topic_score_gemma":0.0001995566,"domain_scores_codex":[0.9975651,0.0002004456,0.0007621816,0.0009972539,0.00005857085,0.0004164476],"domain_scores_gemma":[0.9979075,0.000211726,0.0003423872,0.00136451,0.00006373769,0.000110149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000775614,0.001152094,0.6612222,0.00007396557,0.0001233626,0.00000307404,0.000848158,0.0008117042,0.0002211495,0.1111009,0.0001033214,0.2242626],"study_design_scores_gemma":[0.001648674,0.0002579381,0.535995,0.00003162415,0.000005245876,0.00001929504,0.001307205,0.01950537,0.001804759,0.01848754,0.4201924,0.0007449542],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.780373,0.0001883102,0.00005908929,0.000198979,0.0003673785,0.0002642136,0.0001860554,0.0000157202,0.2183472],"genre_scores_gemma":[0.9964147,0.0007886339,0.001808062,0.00001630145,0.0001515083,0.00002699842,0.0001012606,0.0000257866,0.0006667429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4200891,"threshold_uncertainty_score":0.7921413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1154576560812478,"score_gpt":0.3209856114213498,"score_spread":0.2055279553401021,"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."}}