{"id":"W2801514619","doi":"10.1080/01605682.2018.1457482","title":"A secondary goal in DEA cross-efficiency evaluation: A “one home run is much better than two doubles” criterion","year":2018,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Data envelopment analysis; Weighting; Mathematical optimization; Ranking (information retrieval); Efficiency; Computer science; Cardinality (data modeling); Ideal point; Set (abstract data type); Point (geometry); Operations research; Mathematics; Statistics; Data mining; Estimator; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.05601001,0.0001835373,0.0004020848,0.0004874653,0.001509749,0.001746841,0.00239756,0.0001461388,0.00396514],"category_scores_gemma":[0.005751906,0.0001127381,0.0006526313,0.003573228,0.001321299,0.001321184,0.0005727722,0.001320662,0.0002318879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006649818,"about_ca_system_score_gemma":0.002860902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008175884,"about_ca_topic_score_gemma":0.0001440086,"domain_scores_codex":[0.9839548,0.001933388,0.001525639,0.000534807,0.01145065,0.0006006885],"domain_scores_gemma":[0.9868702,0.001770965,0.0004830428,0.0007812086,0.00990713,0.0001874974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001267941,0.003884487,0.3767269,0.00005923903,0.0007102147,0.00005180497,0.0633013,0.01994008,0.11995,0.00513048,0.366834,0.04214355],"study_design_scores_gemma":[0.006594245,0.0009551987,0.4906665,0.0003392536,0.00008115958,0.0001994499,0.00348493,0.3985422,0.009762397,0.07140359,0.01736102,0.0006100436],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704012,0.0003895829,0.001206384,0.0255442,0.000534665,0.0003211418,0.00001921594,0.000004609326,0.001578963],"genre_scores_gemma":[0.9924861,0.00002336926,0.002703705,0.002028772,0.001129129,0.00001225178,0.000002348682,0.00001456809,0.001599799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3786021,"threshold_uncertainty_score":0.9997901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2150128802638039,"score_gpt":0.5223569650793601,"score_spread":0.3073440848155562,"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."}}