Aha! Trick Questions, Independence, and the Epistemology of Disagreement
Bibliographic record
Abstract
We present a family of counter-examples to David Christensen's Independence Criterion, which is central to the epistemology of disagreement. Roughly, independence requires that, when you assess whether to revise your credence in P upon discovering that someone disagrees with you, you shouldn't rely on the reasoning that lead you to your initial credence in P. To do so would beg the question against your interlocutor. Our counter-examples involve questions where, in the course of your reasoning, you almost fall for an easy-to-miss trick. We argue that you can use the step in your reasoning where you (barely) caught the trick as evidence that someone of your general competence level (your interlocutor) likely fell for it. Our cases show that it's permissible to use your reasoning about disputed matters to disregard an interlocutor's disagreement, so long as that reasoning is embedded in the right sort of explanation of why she finds the disputed conclusion plausible, even though it's false.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".