Bibliographic record
Abstract
A woman glances at a broken clock and comes to believe it is a quarter past seven. Yet, despite the broken clock, it really does happen to be a quarter past seven. Her belief is true, but it isn't knowledge. This is a classic illustration of a central problem in epistemology: determining what knowledge requires in addition to true belief. This book finds a new solution to the problem in the observation that whenever someone has a true belief but not knowledge, there is some significant aspect of the situation about which she lacks true beliefs—something important that she doesn't quite “get.” This may seem a modest point but, as the book shows, it has the potential to reorient the theory of knowledge. Whether a true belief counts as knowledge depends on the importance of the information one does or doesn't have. This means that questions of knowledge cannot be separated from questions about human concerns and values. It also means that, contrary to what is often thought, there is no privileged way of coming to know. Knowledge is a mutt. Proper pedigree is not required. What matters is that one doesn't lack important nearby information. Challenging some of the central assumptions of contemporary epistemology, this is an original and important account of knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".