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Record W4321615342 · doi:10.56094/jss.v58i1.215

Incremental Assurance Through Eliminative Argumentation

2023· article· en· W4321615342 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

VenueJournal of System Safety · 2023
Typearticle
Languageen
FieldEngineering
TopicSafety Systems Engineering in Autonomy
Canadian institutionsCritical Systems Labs
FundersCarnegie Mellon UniversityU.S. Department of Defense
KeywordsQuality assuranceArgumentation theoryCertificationArgument (complex analysis)Safety casePlan (archaeology)Computer scienceProcess managementRisk analysis (engineering)Field (mathematics)Point (geometry)Operations managementMedicineBusinessEngineeringPolitical scienceLawMathematicsEpistemology

Abstract

fetched live from OpenAlex

An assurance case for a critical system is valid for that system at a particular point in time, such as when the system is delivered to a certification authority for review. The argument is structured around evidence that exists at that point in time. However, modern assurance cases are rarely one-off exercises. More information might become available (e.g., field data) that could strengthen (or weaken) the validity of the case. This paper proposes the notion of incremental assurance wherein the assurance case structure includes both the currently available evidence and a plan for incrementally increasing confidence in the system as additional or higher quality evidence becomes available. Such evidence is needed to further reduce doubts engineers or reviewers might have. This paper formalizes the idea of incremental assurance through an argumentation pattern. The concept of incremental assurance is demonstrated by applying the pattern to part of a safety assurance case for an air traffic control system.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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