Bibliographic record
Abstract
I argue that although Paul Grice’s picture of conversational maxims and conversational implicature is an immensely useful theoretical tool, his view about the nature of the maxims is misguided. Grice portrays conversational maxims as tenets of rationality, but I will contend that they are best seen as social norms. I develop this proposal in connection to Philip Pettit’s account of social norms, with the result that conversational maxims are seen as grounded in practices of social approval and disapproval within a given group. This shift to seeing conversational maxims as social norms has several advantages. First, it allows us to neatly accommodate possible variation with respect to the maxims across well-functioning linguistic groups. Second, it facilitates a more psychologically plausible account of flouting. And third, it generates insights about the nature of social norms themselves.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".