Output deterioration with input reduction in 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
In most practical applications of Data Envelopment Analysis (DEA), it is recognized that there is a gap between the theoretical maximum reduction in inputs, e.g., 1 m θ, versus what is actually achievable. In this paper we examine this phenomenon in the context of productivity measurement of highway maintenance crews. Here, maintenance supervisors and geotechnical engineers estimate the maximum achievable reduction in resources without impacting the outputs from the process. Furthermore, they put bounds on the extent of output erosion that can result from input reduction beyond this maximum level. We present a modified version of the standard Banker, Charnes and Cooper (BCC) DEA model that explicitly addresses this erosion phenomenon. The resulting projections provide maintenance managers with a measure of the impact on system performance under excessive resource reduction, and aid in setting guidelines for maintenance budgeting at the patrol level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it