Theoreticity, Underdetermination, and the Disregard for Bizarre Scientific Hypotheses
Bibliographic record
Abstract
Theproblem of scientific disregardis the problem of accounting for why some putative theories that appear to be well-supported by empirical evidence nevertheless play no role in the scientific enterprise. Laudan and Leplin suggest (and Hoefer and Rosenberg concur) that at least some of these putative theories fail to be genuine theoretical rivals because they lack some non-empirical property oftheoreticity.This solution also supports their repudiation of the thesis of underdetermination. I argue that the attempt to provide criteria of theoreticity fails, that there is a Bayesian solution to the problem of scientific disregard that fares better, and that this successful solution supports a distinctively Bayesian version of the underdetermination thesis.
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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 | 0.088 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.077 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".