Bibliographic record
Abstract
Ridicule can be used in order to create concurrence as well as to en-hance antagonism. This paper deals with ridicule that is used by a critic when he is responding to a standpoint or to a reason advanced in support of a standpoint. Ridicule profits from humor’s good repu-tation, and correctly so, even when it is used in argumentative contexts. However, ridicule can be harmful to a discussion. This paper will deal with ridicule from the perspective of strategic maneuvering between the individual rhetorical objec-tive of effecting persuasion and the shared dialectical objective of resolving the dispute on its merits. In what ways can ridicule be used in strategic maneuvering and under what conditions are these uses dialectically sound?
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".