The Sharpness of Their Knives: Interrogating The Rhetoric of Rhetoric of Science
Bibliographic record
Abstract
They 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-
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".