Humanistic Knowledge-Making and the Rhetoric of Literary Criticism: Special Topoi Meet Rhetorical Action
Bibliographic record
Abstract
This article examines the power of special topoi to characterize the discourse of literary criticism, and through emphasis on rhetorical action, it sheds light on the limitations of topos analysis for characterizing research articles in disciplinary discourse more generally. Using an analytical approach drawn both from studies of topoi in disciplinary discourse and rhetorical genre theory, I examine a representative corpus of 21st-century literary research articles. I find that while most of the special topoi recognized by Fahnestock and Secor and Wilder remain prevalent in recent criticism, contemporary literary critics tend to draw on only a select subset of those topoi when making claims about their rhetorical actions. The topoi they use most often— mistaken-critic and paradigm—help identify the ways knowledge-making work is undertaken in literary criticism, a discipline often considered epideictic rather than epistemic. But what the special topoi do not capture is precisely the distinctly motivated, actively epistemic character of this disciplinary rhetoric. Based on these findings, I suggest that special topoi must be seen as functioning in the context of the rhetorical action undertaken by literary research articles. These articles undertake not simply persuasion but the particularly humanistic act I refer to as contributing to scholarly understanding: a rhetorical action worth attending to for scholars of disciplinary discourse, because it is deliberately more concerned with practice than product.
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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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.010 | 0.077 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".