Audit and self‐assessment in quality management: comparison and compatibility
Bibliographic record
Abstract
In recent years, two performance evaluation methodologies have received significant attention in managerial circles: quality audit and self‐assessment. While the quality audit examines the compliance of a quality system with ISO 9000 standards and its suitability to achieve stated objectives, the self‐assessment measures organizational performance against a selected business excellence model. In a continuous improvement effort, an organization can lay out the groundwork by establishing an ISO 9000 quality system, and subsequently use an excellence model to enhance performance, thereby effectively applying both evaluation methodologies. This paper compares the principles and practices of quality audits and self‐assessments, for the purpose of examining their compatibility and providing the basis for integration. Numerous differences in the concepts, purpose, scope and methodology are illustrated, and self‐assessments are found to be more advantageous in enabling continuous improvement. However, it is concluded that audits and self‐assessments are compatible, and further research into the issues of enhancing both methodologies is suggested.
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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 | 0.070 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".