The secret of rendering signs effective: the import of C. S. Peirce’s semiotic rhetoric
Bibliographic record
Abstract
In this article I trace the historical development of Peirce’s semiotic rhetoric from its early appearance as a sub-discipline of symbolistic to its mature incarnation as one of the three main branches of the science of semiotic, and argue that this change in status is a symptom of Peirce’s broadening semiotic interest. The article shows how the evolution of Peirce’s theory of signs is linked to changes in his conception of logic. This modification is not merely a minor justification in his classification of the sciences; rather, it indicates a growing understanding of the interconnection between the different semiotic sub-disciplines. The scope and character of the mature discipline of rhetoric is further discussed in terms of a possible clash between rhetorical and methodological emphases, and a conciliatory strategy is suggested. The article concludes with some reflections on the relevance of Peircean rhetoric for future work in Peirce studies and semiotics.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".