Bibliographic record
Abstract
The paper tries to show that when the deepest or foundational aspects of truth are at issue, both consequentially logical argument and rhetoric that aims to establish truth or justified conviction must engage with the being, or the irreplaceable particularity, of its audience’s members and also that of the arguer, what we refer to in ordinary language as who the person is. Beyond the existing discussion of existential rhetoric, the paper argues that this engagement with being is necessary to establish not only truth that directly concerns or turns on the arguer’s and audience’s being, but also truth or justification about fundamental aspects of things and issues in general. Further, the address of being requires us to suspend both our own and our addressees’ familiar conceptual frameworks in order to allow being to emerge in its own terms. As a result, in contrast with our usual understanding of argumentation, the rhetorician’s initial aim and procedure will be to achieve a genuine suspension of conviction and even of the appropriate concepts under which to proceed, and so to produce a fundamental confusion. The paper then outlines some consequences for rhetoric and reasoning and also the structure of the process of working with this fundamental confusion.
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 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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.077 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".