Bibliographic record
Abstract
In this paper, I consider whether there are limits to virtuous argumentation in certain situations. I consider three types of cases: 1) arguing against denier discourses, 2) arguing with people who make bigoted claims, and 3) cases in which marginalised people are expected to exercise virtues of argument from a position of limited agency. For each type of case, I look at where limits to arguing responsibly might be drawn. I argue that there are situations in which we might withdraw from engagement for practical reasons and others in which withdrawing or refraining from engagement is a responsible way to deal with a particular position. Finally, I argue that in the third type of case, expecting the marginalised to argue as though on even terms with the positions of the dominant risks perpetrating argumentative harm.
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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 | 0.063 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.073 |
| Scholarly communication | 0.021 | 0.035 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.012 | 0.016 |
| 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".