Reasoning Under Uncertainty: The Role of Two Informal Fallacies in an Emerging Scientific Inquiry
Bibliographic record
Abstract
lt is now commonplace in fallacy inquiry for many of the traditional informal fallacies to be viewed as reasonable or nonfallacious modes of argument. Central to this evaluative shift has been the attempt to examine traditional fallacies within their wider contexts of use. However, this pragmatic turn in fallacy evaluation is still in its infancy. The true potential of a contextual approach in the evaluation of the fallacies is yet to be explored. I examine how, in the context of scientific inquiry, certain traditional fallacies function by conferring epistemic gains upon inquiry. Specifically, I argue that these fallacies facilitate the progression of inquiry, particularly in the initial stages ofinquiry when the epistemic context is one of uncertainty. The conception of these fallacies that emerges is that of heuristics of reasoning in contexts of epistemic uncertainty.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.075 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".