<scp>Katrin Ettenhuber</scp>. <i>The Logical Renaissance: Literature, Cognition, and Argument, 1479–1630</i>
Bibliographic record
Abstract
Premodern logic, the art of thinking and reasoning, belonged to the trivium, which comprised, with grammar and rhetoric, the traditional disciplinary cluster responsible for teaching the language arts in their entirety. Over the last half century, the study of Renaissance rhetoric has benefitted from many defenders, who have taken umbrage with the Ramist appropriation of invention and arrangement—classical rhetoric’s first two canons—on behalf of humanist dialectic and its concomitant relegation of rhetoric to matters only of the third canon, style: think of Walter Ong’s defense of oral dialogue against the print spatialization of dichotomizing dialectic and Brian Vickers’s defense of figures of speech from the charge of superficial and deceitful ornamentation. Given the vast amount of ensuing scholarship devoted to rhetoric both in theory and practice, no one now needs to defend this subject’s role in parsing the period’s literary texts. But what about the role logic played? It would thus not be hyperbole to say that current scholarship undervalues and underrepresents logic, the final part of the trivium. Ettenhuber’s book makes an important contribution to Renaissance English studies precisely insofar as it voices a coherent and desperately needed advocacy for the widespread influence of logic upon reading and writing.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.030 |
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".