Could Education Quality Audit Enhance Human Resources Management Processes of the Higher Education Institutions?
Bibliographic record
Abstract
This article aims to check whether the quality audit assessment could positively improve human resource management (HRM) practices and processes of the private higher education institutions. A quantitative analytical approach was performed in order to enhance our understandings of the impact of quality audit in terms of HRM. Twenty-six reports of 26 Omani private higher education institutions (HEIs) who already completed the first stage of national accreditation process has been analysed through this research. Researchers were able to notify a certain positive impact in certain areas related to the staff and staff support. The progress observed is partial, as certain sub-areas of assessment were severely criticized and several recommendations of improvement were issued in such regard. Through this research, we were able to conclude that the Omani private HEIs performed very well in the sub-areas of staff profile, severance, promotion, incentives and Omanization. Contrarily, an important number of recommendations was issued regarding the sub-areas of staff organizational climate and retention, human resource (HR) planning and management, professional development and finally recruitment and selection processes.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".