FuzzEA: A Fuzzy Logic Approach to Efficiency Analysis
Bibliographic record
Abstract
We apply fuzzy logic to measure the efficiency of a unit that consumes multiple inputs to produce multiple outputs. We demonstrate how the fuzzy inference process produces an efficiency profile of a producing unit, which can serve as the output of the analysis or which can be “defuzzified” to produce a specific efficiency score. The approach, which we call FuzzEA for Fuzzy Efficiency Analysis, allows the analyst to measure the efficiency of an individual unit without collecting detailed data for all comparable units. The approach allows the analyst, in conjunction with the context expert, to incorporate industry-specific experiential knowledge concerning relevant ratios of inputs and outputs, information often known as benchmarks, or “rules of thumb.” Therefore, the analyst may prefer the FuzzEA approach when detailed data on all producing units are either unavailable or cumbersome to collect, or when the analyst requires efficiency results for only one or a small number of units. Finally, context experts may find the results more acceptable because the methodology explicitly incorporates expert experience. We demonstrate the FuzzEA methodology in the context of Major League Baseball teams.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".