{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1083164,0.0009813593,0.00149425,0.05071769,0.001838968,0.009142417,0.002889235,0.001358665,0.007433768],"category_scores_gemma":[0.3913812,0.0008763728,0.0008962256,0.1555271,0.001903894,0.01022751,0.005982675,0.003132725,0.002183862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005991542,"about_ca_system_score_gemma":0.005886918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01061107,"about_ca_topic_score_gemma":0.005241074,"domain_scores_codex":[0.8340186,0.05613761,0.03073227,0.01140176,0.06545099,0.00225865],"domain_scores_gemma":[0.4692251,0.3413526,0.0632553,0.03438559,0.08939435,0.002387053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006177661,0.0002552279,0.2041084,0.008615291,0.001016928,0.0004893311,0.0123277,0.003123092,0.0005261315,0.1984803,0.1418995,0.4285404],"study_design_scores_gemma":[0.0001191154,0.000159009,0.1753328,0.01170759,0.0004296537,0.0005108828,0.01171065,0.001798462,0.002053557,0.03545693,0.7604887,0.0002326134],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1549007,0.07991178,0.09274143,0.02790647,0.003688922,0.003546034,0.3838494,0.001358983,0.2520964],"genre_scores_gemma":[0.5365873,0.0469542,0.07856069,0.005315437,0.001998751,0.007469503,0.3124503,0.001655428,0.009008403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1083164,"threshold_uncertainty_score":0.5728388,"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."}}