Bibliographic record
Abstract
The paradox The lottery paradox is the most powerful of the consistency paradoxes, and the greatest threat to the traditional view of consistency. It draws us into a tangle of thorny issues in epistemology, in particular, issues concerning knowledge and justified belief. But the paradox itself is simple and elegant. It depends on one key philosophical premise, the principle of high probability, which can be stated as: (HP) There is a number n (0.5 < n < 1) such that if P has probability n for S , then S is justified in believing P . (HP) is motivated largely by the threat of scepticism. The moral many contemporary philosophers have drawn from the Cartesian exploration of the foundations of knowledge is that to insist on evidence that confers absolute certainty is to ensure, as an inevitable consequence, a thoroughgoing scepticism concerning the world around us. For any empirical belief admits of the possibility of error. (Although I believe I am now typing on a keyboard, for instance, it is possible, given my evidence, that I am merely a brain in a vat, being stimulated to have certain sensations.) If requiring certainty for justified belief is setting the bar too high, then it seems the only alternative is a retreat to high probability; and since a probability of 1 is associated with complete certainty, there must be some degree of probability less than 1 that is sufficient to warrant belief - which brings us to (HP).
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".