Advances in inverse data envelopment analysis: empowering performance assessment
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
Data envelopment analysis (DEA) is a non-parametric optimization approach that was first introduced by Charnes et al. (1978) and is widely used for assessing the performance and comparative efficiency of decision-making units (DMUs) in both public and private sectors. It has emerged as a success story of management science and has found applications in various domains, including environmental, banking, healthcare, transportation, education, manufacturing, agriculture, energy, sport and tourism. DEA’s popularity has grown rapidly since its inception, and it continues to be a valuable tool for decision-makers in various fields (Emrouznejad & Yang, 2018). Standard DEA models evaluate the relative efficiency of DMUs based on their input and output data, but they do not provide information on estimating the amount of inputs and/or outputs needed to achieve efficiency targets. To determine these data, an inverse DEA model must be solved. This requires the development of appropriate mathematical models that are capable of solving the associate inverse problems. Wei et al. (2000) and Amin et al. (2017) highlighted the importance of solving inverse DEA problems and contributed to the development of related mathematical models. However, the challenge of solving inverse DEA problems is still an ongoing research area, and there is a need for further contributions to expand the knowledge base and improve the effectiveness of these models.
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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 it