MétaCan
Menu
Back to cohort
Record W2939517799

Tool-support of socio-technical coordination in the context of heterogeneous modeling : A research statement and associated roadmap

2018· preprint· en· W2939517799 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

VenueEspace ÉTS (ETS) · 2018
Typepreprint
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsComputer scienceConsistency (knowledge bases)Context (archaeology)Set (abstract data type)Statement (logic)Coherence (philosophical gambling strategy)Systems engineeringModel-driven architectureUnified Modeling LanguageSystems modelingSoftware engineeringProcess managementManagement scienceEngineering managementData scienceEngineeringArtificial intelligenceSoftware
DOInot available

Abstract

fetched live from OpenAlex

The growing complexity of everyday life systems (and devices) over the last decades has forced the industry to use and investigate different development techniques to manage the many different aspects of the systems.In this context, the use of model driven engineering (MDE) has emerged and is now common practice for many engineering disciplines.However, this comes with important challenges.As set of main challenges relates to the fact that different modeling techniques, languages, and tools are required to deal with the different system aspects, and that support is required to ensure consistence and coherence between the different models.This paper identifies a number of the challenges and paints a roadmap on how tooling can support a multi-model integrated way of working.

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)0.005
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: none
Teacher disagreement score0.650
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.053
GPT teacher head0.338
Teacher spread0.284 · 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