Measuring relative efficiency of applied science and technology universities in province of Semnan, Iran and providing suggestions for merging units
Bibliographic record
Abstract
University of applied science and technology has been designed to create a platform for multilateral activities such as industrial, military and academic in developing countries to promote science and scientific research applications. These universities are responsible to promote practical training in quantitative and qualitative indicators and they provide appropriate infrastructure to implement theoretical graduates to solve practical problems to build necessary infrastructure to transfer modern technology into developing countries. During the past few years, there have been tremendous development on these units but some of them have not been efficient. In this paper, we present an empirical study to measure the relative efficiencies of various units of applied science and technology universities using data envelopment analysis. The proposed model of this paper uses two inputs including human resources as well as total assets and two outputs including the number of graduate students as well as operating profit. The results of the study have indicated that some of the units are inefficient and need to be merged with other units to increase the relative efficiency of these universities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".