Bibliographic record
Abstract
This paper illustrates a perennial self‐audit model for quality management system (QMS) improvement. The model is based on the concepts of self‐ and nano‐audits, which directly contradict the two central tenets of classical auditing, namely auditor independence and the discrete nature of the auditing process. The general model is presented first, including a description of the underlying concepts (self‐audit, milli‐audit, micro‐audit and nano‐audit), and an illustration of the model elements and their interrelationships. This is followed by an explanation of the benefits and possible uses of the model. Three specific applications are discussed: QSM upgrade based on standards, facilitation of the transition from minimalistic to excellence‐based business systems, and the provision of support in the integration of function‐specific management systems. Subsequently, the emphasis is shifted towards a real‐life application of the proposed model in a high‐tech company. A demonstration of how this model was used to help the case study company in the transition from the ISO 9001: 1994 to the ISO 9001: 2000 QMS is provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".