Incorporating Multiprocess Performance Standards into the DEA Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data envelopment analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer decision-making units (DMUs). It is particularly useful when no a priori information is available on the trade-offs or relationships among various performance measures. A shortcoming of the DEA model, however, is its inability to provide a measure of absolute performance for the DMUs under investigation. Traditionally, in the service sector, this has not been an issue that one could address, because performance standards in that sector have been difficult to establish. However, in those settings where it has become feasible to develop such standards, it is desirable to build these into DEA performance evaluation, thereby enhancing the capability of the tool. While there have been some attempts to incorporate standards into the DEA structure, these approaches have generally been indirect, in the sense that they have focused primarily on restricting the DEA dual multipliers. This paper introduces a new way of building performance standards into the model. Utilizing the conventional DEA framework and a set of activity matrices, a set of standard DMUs can be generated and incorporated directly into the analysis. We show that under normal circumstances, these generated DMUs are efficient relative to the normal ones, and therefore form a type of outer frontier against which regular units can be evaluated. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it