Implementing Total Quality Management (TQM) on the Higher Education Institutions – A Conceptual Model
Bibliographic record
Abstract
Higher education can play a crucial role in the economic and cultural reconstruction and development of the nations. For hundreds of years, the universities and effective educational systems are development factors and agents of change in their communities. Jordan is one of the pioneer countries in higher education due to its credibility; so many students from Arab and foreign countries come to study in. Over the last ten years, a lot of innovative experiments are being done to improve the performance and introduced several laws and constitutions for both academic and educational standards aimed to further develop and improve its ability to compete consistently by successive Jordanian governments, realizing the importance of this sector for socio-economic and cultural development and this requires an ideal governance and service delivery, but the system of higher education in Jordan must be reshaped, the strength must be maintained, but the weaknesses must be addressed and developed, to serve a new social order, to meet the pressing national needs, and to respond to a context of new realities and opportunities. Through this piece of work, this research paper is a theoretical attempt to explain the implementation of TQM in higher education institutions in Jordan, and deals with issues related to quality in higher education, and identify variables influencing quality in this sector.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".