CAR-DEA: Context-Dependent Assurance Regions in DEA
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
Assurance region (AR) restrictions on multipliers in data envelopment analysis (DEA) have been applied extensively in many performance measurement settings. They facilitate the derivation of multiplier values that reflect the reality of the problem situation under study. In measuring the operational efficiency of bank branches, for example, output multipliers would generally represent unit processing times for branch transactions such as deposits. AR restrictions on these multipliers are intended to ensure that the (multiplier) values assigned to the various outputs are relatively of the proper size. Current AR-DEA models presume that multiplier restrictions apply uniformly across all decision-making units (DMUs) in the analysis set. Such models can have severe shortcomings, however, in those situations where different circumstances prevail for some DMUs than for others. In the context of bank branches, for example, two sets of branches, whose transaction times are known to be different from each other, would generally require different sets of AR restrictions. This paper presents a methodology for incorporating multiple sets of AR restrictions, with each reflecting the context for a particular subset of DMUs. The resulting modified DEA model, referred to as CAR-DEA, evaluates performance in a manner that more accurately captures the circumstances in which the DMUs operate.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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