Bibliographic record
Abstract
Can practical factors influence a subject's position to know? Traditionally this question has been answered in the negative. A subject's position to know proposition p is not thought to improve merely because the subject wants to know p or has certain practical stakes depend on whether p. Appealing to these wants and practical interests while defending a claim to know is thought to be epistemically inappropriate. \nWe argue, to the contrary, that practical factors can influence (i.e. encroach upon) a subject's position to know and can do so in an epistemically appropriate way. The argument we provide is relatively straightforward. We claim that knowledge of a certain set of propositions requires a prior action taken on behalf of the subject. This prior action can be influenced by practical factors and thus practical factors can influence a subject's position to know. Furthermore, we argue that such a move can be epistemically appropriate if it arises in an instance when the evidence and arguments favoring belief -- at least from the subject's own point of view -- are inconclusive. We conclude with an argument that the provided account offers a new framework to defend moral encroachment. \nThe prior action taken on behalf of a subject, when it is both practically influenced and is epistemically appropriate, can be interpreted as a moral action.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.061 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".