Bibliographic record
Abstract
Kripke's discussion in Naming and Necessity strongly suggests that semantic stipulation allows us to have new de re thoughts and make new de re claims. For example, it seems we could name the winning ticket in the next lottery 'Tickie' and thereby come to have singular thoughts about Tickie as opposed to merely general thoughts about the winning ticket (whichever one that is). This, in turn, seems to put us into a position to know that Tickie is the winning ticket. If so, it seems we now know which ticket will win the lottery. So it seems semantic stipulation puts us in a position to have all sorts of knowledge that, intuitively, we don't have. We argue that semantic stipulation does put you in a position to have new singular thoughts, though those thoughts will usually be informationally isolated. We think you also typically know these singular propositions, but it is misleading for you to say that you do since you typically cannot act on your knowledge in the expected way. On the other hand, since you can't act on the information in the right way, perhaps your knowledge is thwarted. If so, you don't know Tickie will win since you can't act on that information in order to (e.g.) buy Tickie and no other ticket. We develop both views and argue that they are preferable to the alternatives.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.005 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".