A study on the quality-embedded efficiency measurement 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
Data envelopment analysis (DEA) has been widely used to measure organizational efficiencies and quality among decision-making units (DMUs). General DEA for efficiency measurement, however, does not consider the positive relation between quality and output factors. In this paper, we propose a new DEA-based efficiency evaluation model that applies the positive relation between quality improvement and profits provision, and we suggest benchmarking information that is separated for quality and quantity improvement, respectively. The proposed model evaluates efficiency using new outputs obtained by multiplying the relative quality score by each DEA-output profit factor. In this way, it provides for more accurate discrimination in performance evaluation as well as a practical benchmark target for the improvement of inefficient DMUs. To illustrate the effectiveness and demonstrate the advantages of the proposed method, we conducted efficiency evaluations and benchmarking for 13 Korean national university hospitals.
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 | 0.075 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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