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 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".