Bibliographic record
Abstract
Brook (1999) identified thought experiments as one of the key elements of philosophy's contribution to the cognitive sciences.In this paper, I tackle the question of how and why thought experiments work, and what exactly it is they do for us when they do work.I propose that thought experiments almost always involve two different theories of the world being compared to show that they do, or more often do not, fit together.Sometimes both theories are clearly articulated in the narrative of the thought experiment, but more often one of the two goes unarticulated -the thought experimenter instead relies on our shared folk theories of the world.The strength of some of the more famous and persuasive thought experiments lies in their ability to show that a given theory runs afoul of these deeply held folk intuitions.I will compare the "Dueling Theories" account of thought experiments to both Brook's "empirical" account and Brown's (1991) platonic account.
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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 | 0.053 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.063 |
| Scholarly communication | 0.009 | 0.032 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| 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".