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Record W2501860639 · doi:10.5539/res.v8n3p269

Uncertainty and Cybernetics

2016· article· en· W2501860639 on OpenAlexvenueno aff
Fateme Farkhari

Bibliographic record

VenueReview of European Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInnovation, Sustainability, Human-Machine Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCyberneticsRelation (database)Element (criminal law)Information flowEntropy (arrow of time)Computer scienceKey (lock)Perspective (graphical)EpistemologyManagement scienceArtificial intelligenceEngineeringPhilosophyComputer securityData miningLawPolitical sciencePhysics

Abstract

fetched live from OpenAlex

<p>Cybernetics is an interdisciplinary science that explores the way of control and relation in machines and animals. The relation is the largest and most key element in cybernetic systems without which monitoring and feedback will be meaningless. Information is the key element of relation. Thus, addressing the concept of information, information flow, and its influencing factors are the most important issues in cybernetics. This study aims to investigate the lack and deficiency in the information or the uncertainty from the perspective of cybernetics. It should be noted that risk increases positive entropy, system instability, leads it towards the maximum chaos and destruction. To avoid it, it needs to use the negative entropy or adding information to the network.</p>

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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.004
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: Review · Consensus signal: none
Teacher disagreement score0.919
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.053
GPT teacher head0.386
Teacher spread0.333 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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