Essence, Absence, Uselessness: Engaging Non-Euro-American Rhetorics Interologically
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".