An Analysis of the Instructional Leadership Model at an Institution of Higher Education in the United Arab Emirates
Bibliographic record
Abstract
Leadership remains key to the successful delivery of instruction. The impact of the global economy and the digitalization of higher education continue to level the playing field for institutions of higher education as the competition for enrollment increases. Universities in the United Arab Emirates are not isolated from these changes as many are determined to emulate models of accepted academic leadership and shared governance in order to obtain institutional and programmatic accreditations from outside the region. This research examined the development, implementation and findings associated with the assessment of one institution’s instructional servant leadership model. The study employed factor analysis (dimension reduction) techniques to identify the underlying components driving responses to an employee quesionaire designed to measure the fundamental aspects of servant leadership. The results identified two major driving forces: (1) a holistic overview of the leadership style and (2) the soft skills associated with intrapersonal interaction. The research reminds leaders of the need to comprehend the power of any or all decisions to distract their followers and of the need to cultivate strong interpersonal skills such as communication and respect for others. Keywords: leadership, servant-leadership, higher education, United Arab Emirates
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".