Total Quality Management Meta-Analysis: Founders, Awards Criteria, and Successful versus Failing Cases in Higher Education
Bibliographic record
Abstract
The purpose of this meta-analysis paper is to give a clear presentation of the Total Quality Management (TQM) characteristics and concepts applicable to the higher education context. The paper presents the TQM concepts analysed by the founders of the TQM literature. Followed by the meta-analysis of the influence of TQM awards to quality management principles and characteristics. Those TQM awards are attractive to many organizations, including higher education. Consequently, their criteria can reshape the quality management concepts and implementation in organizations when they are granted such awards. This paper uses qualitative meta-analysis as a method of conducting a thorough secondary qualitative analysis of primarily qualitative results. In this systematic review procedure, the literature is reviewed as not only an objective means to combine the results of previous studies but also to compare, classify, and deduce conclusions of theTQM major concepts and the applicability of this model to higher education including successful and failing cases. All of the TQM concepts constituting of the TQM characteristics discussed by the TQM founders and also those TQM characteristics developed by the TQM awards’ criteria are analysed from the perspective of the higher education TQM scholars in order to present the applicability or inapplicability of those concepts or characteristics to higher education.
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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 | 0.092 | 0.184 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.048 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".