Building performance standards into data envelopment analysis structures
Bibliographic record
Abstract
Data Envelopment Analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer Decision-Making Units (DMUs). DEA is particularly useful where no a priori information on the trade-offs or relations among various performance measures is available. However, it is very desirable if “evaluation standards,” when they can be established, be incorporated into DEA performance evaluation. This is particularly important when service operations are under investigation, because service standards are generally difficult to establish. A number of approaches have been developed to incorporate evaluation standards into DEA as reported in the literature. These approaches tend to be rather indirect, focusing primarily on the multipliers in the DEA models. This paper introduces a new way of building performance standards directly into the DEA structure. Based upon the conventional DEA model and an activity matrix, a set of standard DMUs can be generated and incorporated directly into the DEA analysis. The proposed approach is applied to a sample of 100 branches of a major Canadian bank where time standards are used to generate a set of standard bank branches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".