The Sharpness of Their Knives: Interrogating The Rhetoric of Rhetoric of Science
Bibliographic record
Abstract
IntroductionsThey speak authoritatively to our commonsense, to our intelligence, to our desire of peace or to our desire of unrest; not seldom to our prejudices, sometimes to our fears, often to our egotism -but always to our credulity.And their words are heard with reverence, for their concern is with weighty matters.(Conrad, p. xlvii) These three collections have in common a desire to explore the limits of rhetoric of science, to probe its weaknesses and test its strengths; the editors leave it to their readers to judge if the business of rhetoric of science can continue in its usual way. 1 Campbell and Benson observe, in a review of one of these books and with what I interpret as just a hint of regret, that it "returns us to the world of rhetorical analysts interested as much in the sharpness of their knives as in any object that( ... ) might be used to carve" (p.83 ), and this same observation rings true for each of these collec-Technostyle vol.16, n° I Hiver 2000
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.056 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| 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".