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Record W2057151707 · doi:10.1353/pew.2015.0013

War as a Problem of Knowledge: Theory of Knowledge in China’s Military Philosophy

2015· article· en· W2057151707 on OpenAlexaff
Barry Allen

Bibliographic record

VenuePhilosophy East and West · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStrategistEpistemologyContext (archaeology)Value (mathematics)Military theoryChinaConsistency (knowledge bases)SociologyMilitary sciencePhilosophyPolitical scienceManagementHistoryLawComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

A singularity of the famous Art of War (孫子兵法) attributed to Sunzi is the way this work conceives of knowledge as a resource for the military strategist. The idea is new in Chinese tradition, and new in the worldwide context of thinking about strategy, where Sunzi’s ideas about the value of knowledge are far in advance of the thinking of Western theorists like Machiavelli or especially Clausewitz. The role of knowledge in the Sunzi theory of strategy and the consistency of what this work says about knowledge with a philosophical idea of knowledge that emerges in Warring States texts of diverse genres are analyzed here.

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)0.003
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 score0.019
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0040.032
Scholarly communication0.0060.009
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.288
Teacher spread0.240 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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