A New Management Model for Higher Education in Dubai-UAE: Quality Management in Alignment with Professional Autonomy
Bibliographic record
Abstract
The purpose of this paper is to develop a model of Total Quality Management (TQM) that does not damage the scholarly values of higher education and which enhances professional autonomy, including academic freedom and academic quality in teaching and research. The theoretical framework includes an interpretive study that examines the functional, structural, and cultural aspects of implementing TQM concepts in higher education and the concepts that should be modified.The paper follows the qualitative approach based on the interpretive epistemological paradigm. This research uses empirical data on higher education quality management practices in their natural setting through an interpretive study. The logic of inquiry adopts inductive reasoning that constructs or evaluates general propositions that are derived from specific examples. Empirical data is collected from faculty members from a sample of two universities, including one public university and one private university in Dubai, the United Arab Emirates (UAE), in order to study academics’ perceptions of TQM in higher education. The methodology involves document analysis and collecting interpretations and narratives in order to study the positive and negative influences of quality management practices on, academics, research and the teaching process. The methodology also includes quantitative analysis using the survey instrument for the sake of triangulation.The designed management model which is the result of this paper takes into consideration the issues caused by TQM as a form of new public management, including overload in teaching, administrative tasks, faculty turnover and the reasons behind this along with the quality of teaching and research. Inapplicable TQM concepts are modified based on traditional university management models in order to fit the higher educational context, taking into account higher education leadership and the cross-cultural context that universities operate in especially in Dubai.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".