Scottish dentistry and broken promises: Woollard on presuppositions and substantial negative facts
Bibliographic record
Abstract
Abstract Fiona Woollard claims that negative facts are parts of sequences leading to upshots when they are contrary to the presuppositions of the local community. There are three problems with Woollard’s use of presuppositions. The first is that it fails to capture an important part of our everyday understanding of doing and allowing. The second is that negative facts can be suitable to be parts of sequences even when they accord with presuppositions. The third is that even when negative facts are contrary to presuppositions, this need not be what makes them suitable to be parts of sequences of facts.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.056 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".