The Perspectives of Academic Leaders on Positioning Higher Education for the Knowledge Economy in Oman: Challenges and Opportunities
Bibliographic record
Abstract
Oman Vision 2040 strives to continue shifting the country from an oil to a non-oil-based economy by emphasizing the value of diversification in HEIs. This study aims to provide in-depth insights into implementing the knowledge economy strategy in Oman's higher education system and uses a case study methodology, draws on thematic analysis, and adopts an interpretive perspective employing semi-structured interviews conducted with the dean, assistant deans, and heads of academic departments. The study manifests a common sense among the academic leaders that espousing the policy of the KE in HE is agreed upon. This approbation of KE is ascribed to the discrepancy between developed and developing countries concerning the reality of the higher education systems and the embedded influence of neoliberalism. The study identifies the four fundamental pillars of KE in HE, whereby the study could interpret the existing educational leadership style undertaken and speculate on its future directions. The research indicates three obstacles facing educational leadership; challenges of practicing leadership, lack sustainable system (within the college), and external factors (beyond the college). It also unveils two significant benefits: preparing students for employment in the private sector will reduce the burden on the government to provide careers. Second, the potential reciprocal gains that are pursued in industry and HEIs through funding research, reviewing curriculum and learning outcomes together, and offering a hands-on approach to tackle challenges confronting the national labor market.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".