Bibliographic record
Abstract
Saul Kripke famously raised two sorts of problems for responses to the meaning skeptic that appealed to how we were disposed to use our words in the past. The first related to the fact that our “dispositions extend to only finitely many cases” while the second related to the fact that most of us have “dispositions to make mistakes.” The second of these problems has produced an enormous, and still growing, literature on the purported “normativity” of meaning, but the first has received (at least comparatively) little attention. It will be argued here, however, that (1) the fact that we can be disposed to make mistakes doesn’t present a serious problem for many disposition-based responses to the skeptic, and (2) considerations of the “finiteness” of our dispositions point, on their own, to an important way that the relation between meaning and use must be understood as “normative.”
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".