Restructuring units in the simultaneous presence of desired and undesired factors
Bibliographic record
Abstract
Inverse data envelopment analysis (DEA) represents a fascinating and applicable topic within the DEA field that provides a tool for decision-makers to set target efficiency levels and identify what needs to change to achieve them. One of the most prevalent strategies to boost the efficiency of units involves unit restructuring, a process capitalizing on the synergy of activities. The focus of this study is the application of inverse DEA to build both a theoretical and practical framework during the restructuring of units that handle both desirable and undesirable data. The framework proposed provides a method to identify the inherited inputs/outputs from units involved in the restructuring process, aiming to achieve optimal efficiency objectives amidst the coexistence of both desirable and undesirable factors. The construction of the framework relies on the principles of inverse DEA and the tool of multi-objective programming. Pareto solutions from multi-objective programming issues are utilized to determine a sufficient condition for estimating both desirable and undesirable data. The proposed approach is evaluated through a case study in the educational.
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.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".