4.6.3 A Self‐assessment Scheme and an Evaluation of its Reliability based on ISO 9004:2000
Bibliographic record
Abstract
Abstract The self‐assessment based on ISO 9004:2000 in quality management system is an important process for the organization that continually improves its performance considering the effectiveness and efficiency of a quality management system. The scheme and process of the internal audit and self‐assessment based on ISO 9001:2000 and ISO 9004:2000 is an issue that continues to demand the attention of researchers in this field. Also, the reliability of self‐assessment results is a remarkable issue. One element of reliability is the extent to which different teams assessing the same project produce similar ratings when presented with the same evidence. This paper presents a self‐assessment scheme conducted during internal audits based on ISO 9004:2000 in Electronics and Telecommunications Research Institute in Korea. Furthermore, this paper evaluates the reliability of a self‐assessment scheme analyzing some results from two assessments between two teams in self‐assessment using Cohen's Kappa coefficient (Cohen 1960, Cohen 1968) and the observed agreement index (Jung 2003). The results indicate that the extent of agreement between the two teams is substantial and the self‐assessment scheme is reliable.
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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 | 0.038 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".