Achieving Knowledge: A Virtue-Theoretic Account of Epistemic Normativity, by John Greco.
Bibliographic record
Abstract
John Greco’s latest book is an impressive achievement. It is an intelligent, rigorous, elegantly written, rewarding, and in many respects persuasive account of the nature of knowledge and epistemic normativity. The book’s central thesis is that knowledge is a kind of success through ability, or in other words, that knowledge is an achievement. The resulting view is a non-deontological, non-evidentialist, reliabilist, and contextualist virtue epistemology that is sensitive to knowledge’s social and practical dimensions, and which offers answers to a host of questions at the heart of contemporary epistemology — about the nature of knowledge, epistemic value, epistemic evaluation, luck, and responsibility. Greco’s views have rightly received considerable attention in recent years, and the publication of Achieving Knowledge will ensure that they continue doing so. Any philosopher working on knowledge, normativity, luck, responsibility, or virtue would do well to study it carefully. Greco fully embraces the value turn in epistemology, offering an account on which the nature and normativity of knowledge go hand in hand. His account tells us in one fell swoop what knowledge is and why it is valuable. Knowledge is success from ability: to know is to believe the truth because you believe from intellectual ability. The ‘because’ marks causal explanation. Knowledge is a specific instance of a familiar kind, namely, success from ability. In general success from ability is a good thing, and better than mere lucky success. This is true across the entire range of our activities: social, athletic, artistic, and intellectual. Knowledge fits right into this pattern, as a central form of intellectual achievement. This is why knowledge is better than mere true belief.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".