The Functional Resonance Analysis Method: A Performance Appraisal Tool for Risk Assessment and Accident Investigation in Complex and Dynamic Socio-Technical Systems
Bibliographic record
Abstract
This article aims at systematically reviewing the entire collection of papers published on the development and application of the functional resonance analysis method (FRAM) in the last decade. The Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) methodology has been utilized as a formal systematic literature review standard for data gathering. The analysis encompassed 47 documents devoted to this subject matter, systematically retrieved from the online database Scopus. The findings revealed the necessity for the development of systemic safety assessment approaches to explain performance variability of complex and dynamic socio-technical systems (risk assessment or accident investigation). Indeed, it is crucial to rigorously assess the performance variability throughout safety appraisal since unexpected performance variability can combine in undesirable manners and consequently denotes a threat for safety and losses of human life. However, the FRAM process has some pros and cons as discussed in this review. Consequently, other assessment methods exist to complement the FRAM process.
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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 | 0.195 | 0.305 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.057 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".