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Record W2520212918 · doi:10.22230/cjc.2016v41n3a3181

Essence, Absence, Uselessness: Engaging Non-Euro-American Rhetorics Interologically

2016· article· en· W2520212918 on OpenAlexvenueno aff
LuMing Mao

Bibliographic record

VenueCanadian Journal of Communication · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnecdoteContextualizationHumanitiesRhetoricPhilosophySociologyArtLiteratureInterpretation (philosophy)Linguistics

Abstract

fetched live from OpenAlex

Using the carpenter story from the Zhuangzi as a representative anecdote, the article argues that the art of re-contextualization is interological in orientation and transformative in practice. Drawing on the rhetoric of Dao, it further sketches out the discursive affinities between the art of re-contextualization with its focus on “facts of usage” and “facts of non-usage,” on the one hand, and the interological sensibility marked by the presence of interbeing and becoming, on the other. The article ends by calling on transcending binary logic and developing new terms of engagement for non-Euro-American rhetorics.Cet article se rapporte à la parabole du charpentier selon Zhuangzi comme anecdote pertinente pour soutenir que l’art de la recontextualisation est interologique en orientation et transformatif en pratique. L’article d’autre part a recours à la rhétorique du Dao afin de décrire les affinités discursives entre, d’une part, un art de la recontextualisation mettant l’accent sur les « faits d’utilisation » et les « faits de non-utilisation » et, d’autre part, une sensibilité interologique marquée par la présence de l’interêtre et du devenir. L’article se termine par l’affirmation qu’il faut transcender une logique binaire et trouver une nouvelle manière d’aborder les rhétoriques qui ne sont pas euro-américaines.

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)0.001
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.240
Teacher spread0.205 · 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
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

Citations2
Published2016
Admission routes1
Has abstractyes

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