Bibliographic record
Abstract
In recent work on a priori justification, one thing about which there is considerable agreement is that the notion of truth in virtue of meaning is bankrupt and infertile. (For the sake of more readable prose, I will use ‘TVM’ as an abbreviation for ‘the notion of truth in virtue of meaning.’) Arguments against the worth of TVM can be found across the entire spectrum of views on the a priori , in the work of uncompromising rationalists (such as BonJour (1998)), of centrist moderates (such as Boghossian (1997)), and of uncompromising empiricists (such as Devitt (2004)). My aim is to dispute this widespread opinion. The outline is as follows: first, §§II-III consist of preliminary stage-setting. Then, in §IV I will argue that some of the most prevalent arguments against the worth of TVM — in particular, one which is given clear expression by Quine (1970), and is recently reinforced by Boghossian (1997) — do not engage with the core idea motivating TVM. After deflecting this charge of incoherence, the aim of §§V-VIII is to work toward developing a useful conception of TVM.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".