Bibliographic record
Abstract
Abstract: We argue that Conces’ and Walters’ tool FM2.0 is valuable but does not show that false dilemma is a formal fallacy. The FM2.0 assumes an ambiguous use of the term ‘formal fallacy’, different from how the term is used in logic, and may show that any argument is a formal fallacy. Moreover, the FM2.0 is developed by using one type of false disjunctive syllogism. However, the adequate application of FM2.0 on false dilemma does not lead to an invalid augmented argument, contrary to what is expected in FM2.0, even assuming the ambiguous use of the term ‘formal fallacy’. Résumé: Nous soutenons que l’outil FM2.0 de Conces et de Walters est utile mais ne démontre pas que le faux dilemme est un sophisme formel. Le FM2.0 suppose une utilisation ambiguë du terme « sophisme formel », différente de son utilisation en logique, et peut montrer que tout argument est un sophisme formel. De plus, le FM2.0 est développé en adoptant un type de faux syllogisme disjonctif. Cependant, l’application adéquate du FM2.0 au faux dilemme ne conduit pas à un argument augmenté non valide, contrairement à ce qui est attendu dans le FM2.0, même en supposant l’utilisation ambiguë du terme « sophisme formel ».
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".