Determine Potential Efficiency of Publications Amount for Engineering Departments Using Data Envelopment Analysis
Bibliographic record
Abstract
This paper investigates the potential efficiency of researches from various departments under a common faculty in terms of their individual publications. The outcome in terms of the number of research publications of the eight departments working under the faculty of engineering at King Abdul-Aziz University was utilized as the case for these investigations. Data Envelopment Analysis (DEA) is utilized for the benchmarking of the research potential efficiency of these departments. The results of these studies are useful to know the potential research efficiency of each department under investigation and enable the respective departments/administration to determine the number of research publications necessary from each department to reach their respective optimal levels. The present study is helpful in diverting the interest of university management towards the quality development in education and research. The study is important as it uses Data Envelopment Analysis (DEA) method to determine the relative efficiency of the publication amount for engineering departments at King Abdul-Aziz University. It further suggests some of the important measures required for the improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".