Bibliographic record
Abstract
This dissertation explores the nature and norms of inquiry. In Chapter 1, I argue that curiosity is an intrinsic desire to know. My argument centers around ascriptions of the form ‘curious to φ’, used to attribute curiosity to see, to hear, to try,to meet, and so on. I argue that this form of curiosity should be understood as a desire to φ where the goal of φ-ing is to acquire knowledge. In Chapter 2, I develop an account of the norms of asking questions. The literature on the norms of asking has focused on speaker-centered norms. For instance, inquirers should not ask questions to which they already know the answer. I offer a range of linguistic data to show that there are addressee-centered norms as well: inquirers should not ask addressees who fall short of a certain epistemic status. That epistemic status, I argue, is knowledge. In Chapter 3, I address a puzzle about a tension between the norms of inquiry and the norms of belief. I propose two resolutions of the puzzle that have important consequences for understanding epistemic normativity. The first is thatepistemic norms are conditional on forming a doxastic attitude to a proposition, but have no say on whether to form a doxastic attitude at all. The second is that both positive and negative epistemic obligations, if they exist at all, are not satisfiable. In Chapter 4, I consider whether there are epistemic, rather than just practical, reasons to inquire. Evidentialists have argued that the answer is no, since there are only epistemic reasons for belief, while others have held that the answer isyes, because inquiry has an epistemic aim. In this chapter, I offer a different view: epistemic reasons are primarily for belief, but they also entail epistemic reasons to inquire when inquiring is needed for believing as we should.
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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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.063 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 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".