Bibliographic record
Abstract
Philosophers have debated whether it is possible to knowledgeably infer a conclusion from a false premise. For example, if a fan believes that the actress’s dress is blue, but the dress is actually green, can the fan knowledgeably infer “the dress is not red” from “the dress is blue?” One aspect of this debate concerns what the intuitively correct verdict is about specific cases such as this. Here, I report a simple behavioral experiment that helps answer this question. The main finding is that people attribute knowledge in cases where a true conclusion is inferred from a false premise. People did this despite judging that the premise was false and unknown. People also viewed the agent as inferring the conclusion from the premise. In closely matched conditions where the conclusion was false, people did not attribute knowledge of the conclusion. These results support the view that the ordinary knowledge concept includes in its extension cases of knowledge inferred from false premises.
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 | 0.023 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.046 |
| Scholarly communication | 0.006 | 0.032 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".