Evaluation of Scientific Research Based on Key Performance Indicators (KPIs): A Case Study in Al-Imam Mohammad Ibn Saud Islamic University
Bibliographic record
Abstract
Several years ago Key Performance Indicators (KPIs) becoming a best measurement practiced by the government sectors. The Ministry of Higher Education in Saudi Arabia opens up to new technology, opportunities, and improved ways to acquire and disseminate scientific teaching and research to bring quality at par with the international standards. KPIs provide quality assurance to the scientific research and higher education. The KPIs are variable and designed specifically for a particular entity such as education, research, finance, operation management etc. Scientific research in Saudi Arabia needs special attention from governing bodies and those who are already involved in scientific research. In case of Al-Imam Muhammad Ibn Saud Islamic University (IMAMU), performance indicators are implemented but with skepticism. In future research, the Ministry of Higher Education, Saudi Arabia should provide the best indicators to measure the performance of Saudi universities by putting some value added in implementation of KPIs. Furthermore, third parties such as government servant and stakeholders should togetherness in performing their jobs to make sure everybody is complying with KPIs sets by its agencies.
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".