Bibliographic record
Abstract
Abstract One potentially ethically relevant feature of an utterance is that utterance's influence on the likelihoods that our future discourses wind up with one Stalnakerian ‘common ground’ or body of shared information rather than another. Such likelihoods matter ethically, so the ways our utterances influence them can matter ethically, despite the fact that such influences are often unintended, and often hard to see. By offering a relatively neutral descriptive framework that can enhance our collective sensitivity to and discussion of ethically, socially, and politically important features of language use, this paper contributes to the ethics of language use. It discusses ways in which utterances can influence the likelihoods of future common grounds by deploying one system of categorization rather than another, and argues that language’s effects on the evolution of discourse can affect the paths to and probabilities of different sorts of consensus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".