A new algorithm for benchmarking in integer data envelopment analysis
Bibliographic record
Abstract
The aim of this study is to investigate the effect of integer data in data envelopment analysis (DEA). The inputs and outputs in different types of DEA are considered to be continuous. In most application-oriented problems, some or all data are integers; and subsequently, the continuous condition of the values is omitted. For example, situations in which the inputs/outputs are representatives of the number of cars, people, etc. In fact, the benchmark unit is artificial and does not contain integer inputs/outputs after projection on the efficiency frontier. By rounding off the projection point, we may lose the feasibility or end up having inefficient DMU. In such cases, it is required to provide a benchmark unit such that the considered unit reaches the efficiency. In the present short communication, by proposing a novel algorithm, the projecting of an inefficient DMU is carried out in such a way that produced benchmarking takes values with fully integer inputs/outputs.
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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 | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".