Integration of management systems: focus on safety in the nuclear industry
Bibliographic record
Abstract
The need to create integrated management systems (IMS) in order to handle the proliferation of management system standards is undeniable. There is also evidence in literature and practice that organizations are slowly starting to tackle the IMS issue, mainly by putting an integrated quality and environmental management system in place. Due to the existence of internationally accepted standards covering these two fields, namely ISO 9000 and 14000 series, such a scope of integration comes as no surprise. However, can and should other systems, for example, the ones for occupational health and safety, dependability, social accountability or complaints handling, be included? What would such an integration mean for the existing organizational structures and how could be it be accomplished? When we attempt to address IMS issues, do we really talk about the integration of standards, systems, both or neither? These and other important questions regarding IMS are addressed here. By means of an example from the nuclear industry, this paper focuses in particular on the integration of a safety management system within an IMS framework. Since safety is of such a paramount importance in nuclear plants, it makes sense to integrate safety requirements within a quality management system, as a possible first step in the integration efforts. Subsequently, other function‐specific requirements may be included to form a “real” IMS.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".