Linguistic analysis of efficiency using fuzzy system theory and data envelopment analysis
Bibliographic record
Abstract
Linguistic Analysis of Efficiency (LAE) is a new theoretical development in the domain of efficiency analysis that brings together the elements of performance evaluation with the elements of benchmarking. In organizational practice, LAE is a visually appealing tool, which, based on comparative efficiency evaluation, assists in choosing the best path for improving productivity while at the same time promotes organizational learning. LAE has been built upon two important theories in today's scientific community—Data Envelopment Analysis (DEA) initiated by Charnes et al. [Char78] and Fuzzy System Theory (FST) originated by Zadeh [Zade65]. Combining the two theories, LAE model produces an easy to understand set of natural language rules that describes the shape and characteristics of the standard DEA production space and its efficiency frontier. Once created, these rules then can be easily translated into various forms of informative charts showing the paths toward improving efficiency. LAE thus removes the complexity and abstractness of the DEA process making it more transparent to not only the analyst, but, more importantly, to the decision making unit's (DMUs) manager or the new DMUs that may be created. Hence, LAE allows the analyst to interact with (and better understand) the inner workings of DEA, opening the door to new insight for DMUs wishing to improve efficiency and providing, for the first time, a map for new DMUs to follow when starting out.
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How this classification was reachedexpand
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 | 0.031 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.014 | 0.051 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".