Bibliographic record
Abstract
Abstract This paper aims to show that the Knowledge Norm of Assertion (KNA) can lead to trouble in certain dialectical contexts. Suppose a person knows that p but does not know that they know that p. They assert p in compliance with the KNA. Their interlocutor responds: ‘but do you know that p?’ It will be shown that the KNA blocks the original asserter from providing any good response to this perfectly natural follow‐up question, effectively forcing them to retract p from the conversational scoreboard. This finding is not simply of theoretical interest: I will argue that the KNA would allow the retort ‘but do you know that p?’ to be weaponized in strategic communication, serving as a tool for silencing speakers without having to challenge their testimonial contributions on their own merits. Our analysis can thereby provide a new dimension to the study of epistemic injustice, as well as underscoring the importance of considering the norms governing speech acts also from the point of view of non‐ideal social contexts.
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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 | 0.030 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".