Trade‐offs in integer data envelopment analysis
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
Abstract If production trade‐offs—which represent simultaneously feasible exchanges in the inputs and outputs of decision‐making units (DMUs)—are added to an integer production possibility set (IPPS), a new IPPS is produced; conventional axioms of production do not generate a new IPPS, however. This paper develops the axiomatic foundation for data envelopment analysis (DEA) for integer‐value inputs and outputs in the presence of production trade‐offs by introducing a new axiom of “natural trade‐offs.” First, a mixed‐integer linear programming formula called an integer DEA trade‐off (IDEA‐TO) is presented for computing efficiency scores and reference points. The numeration algorithm (NA) method presented in this concept is improved, and an improved numeration algorithm (INA) method for solving integer DEA (IDEA) models is developed. Finally, comparison between the two methods and a generalized INA method for solving the IDEA‐TO model are presented.
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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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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