{"id":"W2134970186","doi":"10.1111/j.1467-6451.2007.00322.x","title":"ROBUSTNESS OF PRODUCTIVITY ESTIMATES<sup>*</sup>","year":2007,"lang":"en","type":"article","venue":"Journal of Industrial Economics","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":389,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Data envelopment analysis; Econometrics; Robustness (evolution); Parametric statistics; Randomness; Computer science; Productivity; Semiparametric model; Nonparametric statistics; Returns to scale; Observational error; Statistics; Mathematics; Economics; Production (economics)","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02011129,0.0001708261,0.0008538362,0.0008961738,0.00009966653,0.0001529116,0.001095406,0.0002356178,0.0001581064],"category_scores_gemma":[0.01276248,0.0001324305,0.0003896512,0.0009326453,0.0002343586,0.0007323907,0.0001084276,0.0005419667,0.0000152089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001884279,"about_ca_system_score_gemma":0.0005957623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001801312,"about_ca_topic_score_gemma":0.00001703275,"domain_scores_codex":[0.9959474,0.0001459971,0.00262028,0.0003070487,0.0006683608,0.0003109392],"domain_scores_gemma":[0.9932643,0.00209528,0.002991891,0.0005550034,0.0009039232,0.0001895974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002891916,0.0001878528,0.01759928,0.000001559071,0.00007797707,0.00001007349,0.0001748724,0.9205775,0.0001194322,0.0003070176,0.002318233,0.05833702],"study_design_scores_gemma":[0.01243575,0.002744916,0.01469423,0.0004273712,0.001025842,0.001435779,0.008140803,0.8149738,0.06905974,0.02364034,0.04936582,0.002055608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9781559,0.00005439472,0.01880773,0.0008554701,0.001220697,0.00009324513,0.000006972492,0.000005615705,0.0007999823],"genre_scores_gemma":[0.9941466,0.000007950031,0.004080607,0.00003026228,0.001592698,1.948408e-7,5.145497e-7,0.00001277854,0.0001284316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1056037,"threshold_uncertainty_score":0.9955534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1378363368783687,"score_gpt":0.355001061142325,"score_spread":0.2171647242639563,"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."}}