Bench Marking System at King Saudi University
Bibliographic record
Abstract
Bench Marking System at King Saudi UniversityAbstract: King Saud University (KSU) identified eight criteria for selection of benchmarking universities to compare the values of KSU-KPIs. This effort has resulted in the selection of 12 benchmarking universities distributed on 5 geographic zones which are: United States of America, Canada, Europe, Southeast Asia, and Australia. These benchmarking universities are: Harvard - California Berkeley - MIT - Sanford - Illinois Urbana Champaign-Toronto - British Columbia - Cambridge - Manchester - National University Singapore - Tokyo – Monash. Several KSU academic and administrative units are participated in building the benchmarking system including: the General Directorate of Endowments, Deanship of Graduate Studies, Deanship of Scientific Research, Deanship of Electronic Transactions and Communications, Deanship of Development, Deanship of Admissions and Registration, Deanship of Student Affairs, Deanship of E-Learning and Distance Learning, Deanship of Skills Development, Deanship of Library Affairs, Deanship of Faculty and Staff Affairs, Deanship of Quality, Department of Measurement and Performance, and Department of Statistics and Information. Several mechanisms have been used by the team, such as meetings, internet research, correspondence, review of the annual reports of the benchmarking universities, and discussions. About 99 benchmarking universities were initially selected, and later short listed into 12. The benchmarking universities of the strategic plan of KSU (2030) are also included in the shortlist of these 12 universities. A model for benchmarking system was built and approved within which the responsibilities, timetable, procedures are determined. There are several lessons learnt from this experience that are: the appropriate choice of benchmarking universities is the basis for development, the use of quantitative methodology helps universities in the planning to close the gap between their performance and that of the benchmarking universities through formulation of specific and measurable objectives.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.112 | 0.045 |
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".