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THREE META-QUESTIONS IN EPISTEMOLOGY: RETHINKING SOME METAPHORS IN ZHUANGZI*

2005· article· en· W2091857898 on OpenAlexaff
Derong Chen

Bibliographic record

VenueJournal of Chinese Philosophy · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Cultural Studies in Latin America and Beyond
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEpistemologyPhilosophyChinese philosophyAsian studiesChinaPolitical scienceLaw

Abstract

fetched live from OpenAlex

The metaphors in the Zhuangzi have a strong appeal to scholars both in China and the West.In this article, I do not intend to discuss the general features of Zhuangzi's metaphors 1 nor the all metaphors in the Zhuangzi.Instead, I shall specifically argue that Zhuangzi metaphorically puts forth three meta-questions or fundamental questions in epistemology: 1) as an epistemic subject, do I know I myself?2) Among epistemic subjects, do I know others?3) What can I know about the world?I shall demonstrate, by reanalyzing some of Zhuangzi's metaphors, that the discovery of these three metaquestions is Zhuangzi's valuable contribution to epistemology.I. Do I Know I Myself: Self-Identification of an Epistemic Subject Do I know I myself?This is an initial question that we should clarify before dealing with any other epistemological issue.Zhuangzi puts this question in his metaphor of the butterfly: Once Chuang Chou [Zhuang Zhou] dreamt he was a butterfly, a butterfly flitting and fluttering around, happy with himself and doing as he pleased.He didn't know he was Chuang Chou.Suddenly he woke up and there he was, solid and unmistakable Chuang Chou.But he didn't know if he was Chuang Chou who had dreamt he was a butterfly, or a butterfly dreaming he was Chuang Chou.Between Chung Chou and a butterfly, there must be some distinction!This is called the Transformation of Things. 2What is the meaning of this story as a metaphor?Between the lines many scholars read that this story tells us about the relationships between soul and body, existence and non-existence, being and nonbeing, life and death, certainty and uncertainty, continuity and dis-

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)0.009
metaresearch head score (Gemma)0.007
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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0060.048
Scholarly communication0.0060.019
Open science0.0010.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.262
Teacher spread0.238 · 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

Citations7
Published2005
Admission routes1
Has abstractyes

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