Documentation of Assumptions and System Vulnerability Monitoring: the Case of System Theoretic Process Analysis (STPA)
Bibliographic record
Abstract
The documentation of assumptions during hazard and risk analysis allows the monitoring of their validity which can function as a leading performance indicator. This paper through a combination of literature references and pragmatic standpoints presents the groups of assumptions which the analyst can make at each discrete step of the System Theoretic Process Analysis (STPA) and elaborates on the connection between invalid assumptions and system vulnerability. Ten assumption groups were identified as possible during the performance of STPA, starting from the system definition and moving to the last activity of the particular technique, namely the generation and testing of causal scenarios. The assumptions were attributed to the boundaries with regard to the scope and resources of the analysis and the inevitable assignment of maintenance of constraints and fulfilment of requirements to agents that are external to the system under study. Also, the impact of the assumptions was linked to the hierarchical system level under the claim that the higher the system level the assumptions are made, the higher the system vulnerability. The assumption groups derived in this study can assist users of STPA and other hazard analysis techniques in the recognition and documentation of assumptions and render their analysis results more credible and transparent. Moreover, the current work might complement hazard analysis guidelines and can be incorporated in software applications that support such analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".