Fallacies: do we “use” them or “commit” them? Or: is all our life just a collection of fallacies?
Bibliographic record
Abstract
ABSTRACT: After C. L. Hamblin's groundbreaking work Fallacies (1970), re-interpreting what used to be known as "mistakes in reasoning " or "bad arguments " since Aristotle (On Sophistical Refutations), the study of fallacies started to bloom, coming up with ever new perspectives and conceptualizations of what should count as a mistake in reasoning and argumentation, and why a certain kind of reasoning should at all be considered a mistake (Woods & Walton 1989, van Eemeren & Grootendorst 1992, etc.). This paper will be concerned with two questions. First, an epistemological one: do we (unintentionally) commit fallacies, or do we (intentionally) use them? Secondly, a methodological one: when we (philosophers, sociologists, discourse analysts,...) detect a fallacy, on what conceptual grounds do we differentiate between committed and used fallacies? Aren't we forced to commit (or use?) "fallacies " whenever we talk about other people, their views, or their work? Examples from Critical Discourse Analysis will be used to extensively illustrate this point.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".