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Record W2942470540 · doi:10.1109/msmc.2019.2899698

The Emergence of Abstract Sciences and Transdisciplinary Advances: Developments in Systems, Man, and Cybernetics

2019· article· en· W2942470540 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

VenueIEEE Systems Man and Cybernetics Magazine · 2019
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCyberneticsTransdisciplinaritySystems scienceCognitive sciencePerceptionHuman scienceEngineering ethicsPhenomenonScience and engineeringEpistemologySociologyManagement scienceComputer scienceArtificial intelligenceSocial scienceEngineeringPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Transdisciplinary studies in systems, man, and cybernetics (SMC) are an advanced approach to generate new knowledge and novel perceptions on persistent challenges and emerging technologies across the edges of traditional disciplines. An unprecedented phenomenon in science history in the past decade is the emergence of abstract sciences (ASs) as a counterpart of classic concrete sciences (CSs). It leads to a new perception of SMC as well as its transdisciplinary foundations and the impacts on classic sciences and engineering disciplines. This article presents the emergence of AS underpinned by SMC and denotational mathematics (DMs). It explores the transdisciplinary theories of system science, cognitive cybernetic foundations of ASs, and hybrid human-machine societies driven by the fast development of artificial intelligence (AI), intelligence science, and knowledge science as well as their engineering applications.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.739

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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