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Record W2806240004 · doi:10.1515/apeiron-2017-0029

Elements and Opposites in Heraclitus

2018· article· en· W2806240004 on OpenAlexaff
Richard Neels

Bibliographic record

VenueApeiron · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical, Religious, and Philosophical Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEpistemologyPhilosophyProperty (philosophy)Relation (database)Transformation (genetics)Extant taxonChemistryComputer science

Abstract

fetched live from OpenAlex

Abstract In this paper, I discuss the various characteristics of Heraclitus’ theory of elemental transformation that can reasonably be gleaned from the extant fragments. While there has been some recent work on Heraclitus’ theory of elemental transformation, there has been a lack of discussion concerning the properties of the particular elements and their relation to the cardinal opposites. In this paper I argue that fragment B126 (“Cold things warm up, warm things cool off; wet things dry up and dry things moisten”) is an explanandum for Heraclitus. It is meant to invoke certain questions in his readers’ minds: How is it that cold things come to hold an opposing property (i. e. “hot”)? What type of change occurs such that a wet thing can become dry? How is it that things hold these properties in the first place? I argue that Heraclitus’ theory of elemental transformation (B31, B36 and B76) is the explanans for B126 and is capable of answering these questions. That is, the observable change evident in B126 is explained by a set of transformations between elemental stuffs. Because of this connection between B126 and his theory of elements, Heraclitus’ theory of elemental transformation is rightly understood as a ‘unity of opposites’ thesis. However, I argue that the transformation of opposites can only be one opposites thesis among several opposites theses.

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)0.005
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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.025
Scholarly communication0.0050.008
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.031
GPT teacher head0.234
Teacher spread0.203 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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