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Record W4398205633 · doi:10.1115/1.887738_ch5

System-Theoretic Process Analysis (STPA): An Innovative Top-Down Hazard Assessment Method for Based on the System-Theoretic Accident Model and Processes (STAMP) - A Bibliometric Review

2024· review· en· W4398205633 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueASME eBooks · 2024
Typereview
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsHydro-QuébecUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsAccident (philosophy)Process (computing)Computer scienceHazardHazard analysisIndustrial engineeringEngineeringReliability engineeringProgramming language

Abstract

fetched live from OpenAlex

This paper seeks to conduct a comprehensive review of the entire collection of documents published on the development and utilization of STAMP/STPA over the past decade. To achieve this, we have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, which serves as a recognized standard for systematic literature reviews in data collection. The study involved nineteen papers devoted to this subject matter, systematically retrieved from the online database Scopus. The findings indicated the need to integrate STAMP/STPA with other systemic safety assessment approaches to create a comprehensive Integrated Decision-Making Framework for Industrial Asset Management. This framework would be instrumental in assessing and managing emerging technology risks, as well as addressing extreme, rare, and disruptive events. Further research would validate its efficiency and practicality. Therefore, future research initiatives will be devoted to conducting case studies in order to obtain more accurate data.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Scholarly communication
Consensus categoriesMeta-epidemiology (narrow), Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.701
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0390.121
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0030.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.125
GPT teacher head0.480
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it