Incorporating Ratios in DEAâApplications to Real Data
Bibliographic record
Abstract
In the standard Data Envelopment Analysis (DEA), the strong disposability and convexity\naxioms along with the variable/constant return to scale assumption provide a good\nestimation of the production possibility set and the efficient frontier. However, when\ndata contains some or all measures represented by ratios, the standard DEA fails to\ngenerate an accurate efficient frontier. This problem has been addressed by a number of\nresearchers and models have been proposed to solve the problem. This thesis proposes a\n“Maximized Slack Model” as a second stage to an existing model. This work implements\na two phase modified model in MATLAB (since no existing DEA software can handle\nratios) and with this new tool, compares the results of our proposed model against the\nresults from two other standard DEA models for a real example with ratio and non-ratio\nmeasures.\nThen we propose different approaches to get a close approximation of the convex hull\nof the production possibility set as well as the frontier when ratio variables are present\non the side of the desired orientation.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".