On the concept of a universal audit of quality and environmental management systems
Bibliographic record
Abstract
Abstract There is a definite trend in industry today toward the integration of internal management systems (MSs), including those for managing quality, environment, health and safety, and social accountability. The standards describing the minimum requirements for such systems have been made largely compatible, but are not yet fully aligned or integrated. Apart from several national standards for integrated quality, environment and safety MSs, the world has yet to see a corresponding and internationally accepted guideline. In contrast, integrative standardization activities in the realm of MS auditing are proceeding in full force, with the introduction of the pioneering ISO 19011 guideline for quality and environmental auditing expected soon. This paper focuses on the concepts, principles and practices of a truly generic audit, applicable for the evaluation of diverse aspects of organizational performance against the criteria stated in MS standards. A universal audit model based on the systems approach and several important questions regarding the compatibility and integration of the current auditing schemes are discussed. These issues include the ability of integrated audits to foster unification of supported MSs, as well as different strategies for the development of a universal audit guideline (UAG) and integration of function‐specific audits. Copyright © 2002 John Wiley & Sons, Ltd. and ERP Environment.
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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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| 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".