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Record W2921605904 · doi:10.5539/jmr.v11n2p92

Dialectical Logic and Boolean Algebra

2019· article· en· W2921605904 on OpenAlexvenueno aff
Yaozhi Jiang

Bibliographic record

VenueJournal of Mathematics Research · 2019
Typearticle
Languageen
FieldComputer Science
TopicLogic, Reasoning, and Knowledge
Canadian institutionsnot available
Fundersnot available
KeywordsDialecticMathematicsTruth valueMany-valued logicBoolean algebraPredicate logicIntuitionistic logicAlgebra over a fieldPhilosophy of logicTruth tableCalculus (dental)Discrete mathematicsEpistemologyPure mathematicsAlgorithmPhilosophyComputer scienceTheoretical computer scienceDescription logicPropositional calculusProgramming language

Abstract

fetched live from OpenAlex

Dialectical logic was founded by German famous philosopher F. Hegel, but it has not been laid on mathematics for a long time. In this paper author explains the dialectical logic pure mathematically, and shows that the classic formal logic, its mathematical expression is Boolean algebra(includes multiple value system), is a special case from dialectical logic, and the true-valued function for dialectical logic is a continuous function valued on closed interval and defined on time-space axes system. The Aristotle three laws of formal logic are expanded into expression of dialectical logic, and Russell paradox is expanded into the case of multiple order. Some new theorems for Boolean operators and the matrix expression for De Morgan’s theorem of multiple variables dialectical logic are given. At the end of the paper, linear or nonlinear dialectical logic are defined and analysis properties of dialectical logic true-valued function are pointed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.006
Scholarly communication0.0030.007
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.082
GPT teacher head0.371
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations1
Published2019
Admission routes1
Has abstractyes

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