Bibliographic record
Abstract
I argue for a view I call sensitivism about belief. According to sensitivism, belief is sensitive to just those factors of context which epistemic contextualists claim are relevant to the semantics of words like "know": in particular, whether an agent believes p depends on the not-p alternatives salient to the agent, and the practical importance of p for the agent. I argue for sensitivism about both outright belief and partial belief, and outline a sensitivist formal model of belief. In chapter 1, I make a preliminary case for sensitivism, and for interest in sensitivism. After surveying some similar views in the literature, I present a scenario which is nicely explained by sensitivism, and which gives the view some intuitive plausibility. I also argue for the relevance of sensitivism to the debate over epistemic contextualism. In chapter 2, I argue that we need sensitivism about outright belief if we want to maintain both a Stalnakerian picture of how assertion works, and the principle that an assertion that p is sincere if and only if the assertor believes that p. I then outline a sensitivist formal model of outright belief. In chapter 3, I present a solution to the preface paradox which this model of belief makes available, and argue that it is more intuitively appealing than the more popular probabilistic solutions. In chapter 4, I argue that we should extend sensitivism to credences as well as outright belief. In particular, I advance the following two theses: (CONTEXT) Degrees of belief change from context to context, depending on the space of alternative possibilities. (UNITY) Outright belief is belief to degree 1. I claim that (UNITY) solves the usual paradoxes to which threshold views of outright belief fall prey, and (CONTEXT) undermines the usual reasons given for rejecting (UNITY).
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".