Bibliographic record
Abstract
The audit profession has been facing reassessment and repositioning for the past decade. Enquiry has been an integral part of an audit; however, its reliability as a source of audit evidence is questioned. To legitimize enquiry in the face of audit complexity and ensure sufficiency, relevance, and reliability, the introduction of Stafford Beer’s Viable System Model (VSM) into theory and practice has been recommended by a number of authors. In this paper, a variant on previous VSM-based audit work is introduced to perfect auditing assessment of accountability and compliance. This variant is termed the “VSM/NVA variant” and is applicable when the VSM model is in use for an audit. This variant is based on application of Network Visualization Analysis (NVA) to a VSM-modeled organization. Using NVA, “decision leaders” can be identified and their socio-technical relevance to VSM systems explored. This paper shows how the concepts of decision leaders and their networks can enrich and clarify practical applications of audit theory and practice. The approach provides an enhanced real-world understanding of how various VSM systems and network layers of an organization coalesce, and how they relate to the aims of the VSM model at micro and macro levels.
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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".