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Record W2076573027 · doi:10.1145/1595453.1595455

Formal methods versus engineering

2009· article· en· W2076573027 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

VenueACM SIGCSE Bulletin · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEngineering mathematicsScience and engineeringPosition (finance)Formal methodsDomain (mathematical analysis)Computer scienceKey (lock)Software engineeringMathematics educationManagement scienceApplied mathematicsCalculus (dental)MathematicsEngineeringEngineering ethics

Abstract

fetched live from OpenAlex

Classical engineering is based on solid scientific and mathematical foundations, but neither the science, nor the mathematics, is simply borrowed from the scientists or the mathematicians. Engineers develop their own formulations of the relevant science and mathematics, adapted to support the engineering knowledge used in design of artefacts. There are many formulations of the same science and mathematics, as classical engineering is highly domain specific. A key question for Formal Methods Education is whether uses and formulations of mathematics used in software engineering should be analogous to the situation in classical engineering described above. The position advocated in this paper is that the classical engineering approach is also crucial for Formal Methods. We may well not be in a position to teach a proper formulation of formal methods until we have developed the appropriate reformulations of the usually mathematically oriented mathematics usually taught in computer science and software engineering programmes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.027
GPT teacher head0.277
Teacher spread0.249 · 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