Systems thinking for the integration of management systems
Bibliographic record
Abstract
This paper discusses how a systems approach to management can be used to facilitate the development and implementation of an integrated management system (IMS) in an organization. It is argued that any solution to address the rapidly growing need for the integration of function‐specific management systems requires two elements: a conceptual model and a supporting methodology. While the research on IMS modelling is fairly advanced, evidenced by a number of existing models that would probably qualify to provide the basis for integration, development of methodologies to achieve fully‐integrated systems is still lacking. This paper therefore provides a set of criteria for selection of the most appropriate IMS model, followed by a discussion of one such model based on the systems approach. The presented model can be used to integrate the requirements of existing and upcoming function‐specific management system standards, and provide a foundation for the top‐down integration of internal systems that these standards describe. Subsequently, a short discussion on the issue of the IMS methodology is given, and the paper concludes with a list of questions that will help researchers design a comprehensive IMS methodology.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".