Bibliographic record
Abstract
Abstract This book is about our practice of criticizing one another for epistemic failings. We clearly evaluate and critique one another for forming unjustified beliefs, harboring biases, and pursuing faulty methods of inquiry. But what is the nature of this criticism? Does it ever rise to the level of blame? The question is puzzling because there are competing sources of pressure in our intuitions about “epistemic blame,” ones not easy to reconcile. The more blame-like a response is, the less at home in the epistemic domain it seems—but the more at home in the epistemic domain a response is, the less blame-like it seems. These competing sources of pressure constitute a puzzle about epistemic blame. The most promising solution to this puzzle focuses on the interpersonal side of epistemic normativity. Members of an epistemic community stand in an “epistemic relationship,” and epistemic blame is a way of modifying this relationship. Understanding epistemic blame as a distinctive kind of relationship modification locates a response that is both robustly blame-like, and entirely at home in the epistemic domain. Epistemic relationships can also illuminate a unique set of issues in the “ethics of epistemic blame,” ones that mirror corresponding issues in the ethics of moral blame. The book examines the scope of appropriate epistemic blame, standing to epistemically blame, and the value of epistemic blame in our social and political lives. Throughout the investigation, a better understanding of the parallels and points of interaction between the epistemic and other normative domains emerges.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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".