Total Quality Management: A pictorial guide for managers
Bibliographic record
Abstract
Another new book in the popular and original series of pictorial guides - John Oakland cuts through the complex concepts and confusing jargon associated with implementing Total Quality, and Peter Morris presents the information in his inimitable pictorial style. This book will show students and managers what they need to understand about TQM in the simplest, clearest and most memorable form. Professor John Oakland is undoubtedly the British guru of quality management. Following a successful industrial career in research and production management, he has developed a pragmatic approach to introducing TQM which he and his colleagues have used successfully in literally thousands of organizations. He is founder and Executive Chairman of OAKLAND Consulting Plc. and Head of the European Centre for TQM at the University of Bradford Management Centre. Also published by Butterworth-Heinemann are John Oakland's bestselling Total Quality Management (now in its second edition) and Cases in Total Quality Management. Peter Morris is the creative force behind the illustrations in all Butterworth-Heinemann's pictorial guides. Originally trained as an art teacher, he spent several years as an industrial designer in Canada before returning to England to design educational and training materials for the University of Sussex. His experience working on industrial contracts convinced him, quite rightly, that cartoons are frequently the best way to illustrate the abstractions of business life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.052 |
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".