{"id":"W2797350939","doi":"10.1016/j.omega.2018.04.004","title":"DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans","year":2018,"lang":"en","type":"article","venue":"Omega","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Benchmarking; Incentive; Data envelopment analysis; Payment; Context (archaeology); Set (abstract data type); Performance measurement; Incentive program; Business; Computer science; Pay for performance; Operations research; Environmental economics; Economics; Finance; Microeconomics; Marketing; Engineering; Mathematics; Mathematical optimization","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":[0.008593011,0.000162732,0.0002827039,0.0006034911,0.0004143064,0.0001606503,0.0006275457,0.00009297046,0.0001153422],"category_scores_gemma":[0.001850336,0.0001178275,0.0001150803,0.001528728,0.0001499865,0.0004798913,0.00004792745,0.0001082777,0.0001071848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001922297,"about_ca_system_score_gemma":0.0002780534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001647556,"about_ca_topic_score_gemma":0.0003093577,"domain_scores_codex":[0.9968247,0.0001363755,0.0006737777,0.0006065801,0.001324485,0.0004341293],"domain_scores_gemma":[0.9968625,0.001246718,0.0003181518,0.0005239276,0.0009906247,0.00005803349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004847432,0.0001816826,0.4350232,0.00003737217,0.00002499026,5.896713e-7,0.001495593,0.05188926,0.0008480001,0.0003218088,0.002593388,0.5070993],"study_design_scores_gemma":[0.0009307924,0.0003093668,0.09862439,0.00006874587,0.00002623751,3.869885e-7,0.00008202274,0.8845129,0.004187807,0.0006513701,0.01042879,0.0001772235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703171,0.00002842609,0.02639119,0.0002717126,0.0006079282,0.0006450582,0.00002360305,0.00002338577,0.00169154],"genre_scores_gemma":[0.9949062,0.000003944014,0.00413448,0.0002637005,0.0002616573,0.0001511938,0.00003206804,0.00001271512,0.0002339775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8326236,"threshold_uncertainty_score":0.4804867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1036606648969862,"score_gpt":0.3956076374766312,"score_spread":0.2919469725796451,"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."}}