Inductive Risk and Epistemically Detrimental Dissent in Policy-Relevant Science
Bibliographic record
Abstract
While dissent is key to successful science, it is clear that it is not always beneficial. By requiring scientists to respond to objections, epistemically detrimental dissent (EDD) consumes resources that could be better devoted to furthering scientific discovery. Moreover, bad-faith dissent can create a chilling effect on certain lines of inquiry and make settled controversies seem open to debate. Such dissent results in harm to scientific progress and the public policy that depends on this science. While Biddle and Leuschner propose four criteria that draw on inductive risk as a method for separating this EDD from beneficial dissent, de Melo-Martín and Intemann reject this approach for failing to capture paradigmatic instances of EDD. Against de Melo-Martín and Intemann’s objections, I propose the inductive risk account can be saved and strengthened through the following modifications: 1) removing the requirement that the four conditions of EDD be jointly satisfied, 2) requiring that each criterion be measured as a matter of degree rather than as a binary, and 3) requiring that the four criteria are measured holistically. These modifications not only mitigate the criticisms but produce five benefits over Biddle and Leuschner’s account, including: 1) capturing paradigmatic instances of EDD, 2) reflecting the degree to which an instance of EDD is problematic, 3) capturing the interactions between criteria, 4) avoiding legitimizing inappropriate dissent, and 5) reflecting changes to the epistemic standing of dissent. As such, I argue that the modified IndRA provides a powerful tool for identifying EDD and strengthening science.
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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 | 0.090 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".