Bibliographic record
Abstract
Radical skepticism is the view that we know nothing, or at least next to nothing. Nearly no one actually believes that skepticism is true. Yet it has remained a serious topic of discussion for millennia and it looms large in popular culture. What explains its persistent and widespread appeal? How does the skeptic get us to doubt what we ordinarily take ourselves to know? I present evidence from two experiments that classic skeptical arguments gain potency from an interaction between two factors. First, people evaluate inferential belief more harshly than perceptual belief. Second, people evaluate inferential belief more harshly when its content is negative (i.e. that something is not the case) than when it’s positive (i.e. that something is the case). It just so happens that potent skeptical arguments tend to focus our attention on negative inferential beliefs, and we are especially prone to doubt that such beliefs count as knowledge. That is, our cognitive evaluations are biased against this specific combination of source and content. The skeptic sows seeds of doubt by exploiting this feature of our psychology.
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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 | 0.013 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".