Bibliographic record
Abstract
Several philosophers have recently developed accounts of relative (or assessment context-sensitive) truth. Given that logical consequence is often characterized in terms of truth preservation, notions of truth are often associated with corresponding notions of logical consequence. Accordingly, in his Assessment Sensitivity: Relative Truth and Its Applications, John MacFarlane provides two different definitions of logical consequence that incorapte assessment context-sensitive truth. One motivation for adopting an assessment context-sensitive account of truth for judgements about taste is to explain how conflicting taste claims can be true relative to different contexts of assessment. However, in the midst of dialogues in which conflicting taste claims are made, it is also possible for the participants in the dialogue to make conflicting claims about what inferences are and are not logically valid. This paper accomplishes two objectives. First, I argue that MacFarlane’s notions of logical consequence do not adequately account for important features of some dialogues in which conflicting logical claims are made. In particular, I argue that MacFarlane’s accounts of logical consequence do not explain how logical claims made about inferences in taste-discourse could be assessment context-sensitive. Second, I propose a consequence relation that can be incorporated into an assessment context-sensitive account of logical claims made about inferences in taste discourse.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.007 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".