The true, the good and the justified: Essays on epistemic normativity and value
Bibliographic record
Abstract
This dissertation about the role that value—in particular, the value of truth—plays in the explanation of epistemic norms, the norms that should govern our belief-formation and belief-revision processes. It is a pervasive practice for us to assess one another’s beliefs, decrying some and commending others: we find something amiss in a belief formed by wishful thinking, and deem proper a belief arrived at via meticulous consideration of one’s evidence. It is natural to think that there is a close connection between what epistemic norms sanction, truth, and the fact that truth matters. In what sense is truth valuable? and what does that entail for how we should conceive of epistemic norms? These questions drive my dissertation. In chapter 1, “The true, the good and the justified”, I argue that the teleological conception of epistemic justification, the view that for a belief to be justified is for it to be formed in a way that is conducive to truth, is safe from the main objections it faces. These import assumptions about value that belong to ethical consequentialism; the epistemic teleologist need not and should not accept them. “Epistemic value” is best understood as a ‘placeholder’, not as a term denoting value in any substantive sense. The upshot is that endorsing a teleological explanation of epistemic norms does not commit one to the idea that true beliefs ought to be promoted in the way the good ought to be promoted according to ethical consequentialists. In chapter 2, “Why be antisocial (about epistemic normativity)”, I examine whether epistemic normativity is grounded in the usefulness of truth for communities and argue that it is not. If what we ought to believe were to be explained in terms of the usefulness of truth for communities, we should expect our epistemic norms to be wildly different from what they are: they could condone trade-offs between true and false beliefs across a community when doing so would be useful for it; they would often fail to prescribe believing over other doxastic attitudes when the epistemic aims of the community would be equally well-served by either; finally, there is no way to answer in a satisfying manner the question of what isolated individuals should believe. In chapter 3, “What’s truth got to do with it?”, I develop an account of epistemic normativity that does justice to the idea that truth matters but avoids the shortcomings of other value-based accounts. I argue that, given any plausible account of the value of truth, the value of truth can explain some, but not all epistemic norms. The account of epistemic normativity that I present is pluralistic: I distinguish between substantive norms, which are explained by the value of truth, and basal norms, which stem from the good functioning of mechanisms of belief-formation and belief-revision, independently of the value of truth. This account is shown to be superior to other accounts discussed in the course of the dissertation.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".