An Experimental Study on the Evaluation of Metaphorical Ad Hominem Arguments
Bibliographic record
Abstract
Abstract: Metaphors are emotionally engaging, influenc-ing the evaluation of arguments. The paper empirically in-vestigates whether metaphors in the premise can lead the evaluator to judge an ad hominem argument as sound when the arguer instead committed a fallacy. The results show that ad hominem arguments with conventional and positive metaphors are more persuasive compared to those with novel and negative metaphors. Arguments with conventional metaphors are also perceived as more am-biguous, but less convincing, and emotionally appealing. Additionally, participants believe in the conclusion more when the premise contains a positive rather than a nega-tive metaphor, which instead helps the evaluator detect the fallacy. Résumé: Les métaphores sont émotionnellement engag-eantes et ainsi influencent l’évaluation des arguments. L'article étudie empiriquement si les métaphores em-ployées dans la prémisse peuvent amener l'évaluateur à juger un argument ad hominem comme solide bien que ce sophisme soit commis. Les résultats montrent que les ar-guments ad hominem utilisant des métaphores conven-tionnelles et positives sont plus convaincants que ceux utilisant des métaphores nouvelles et négatives. Les argu-ments utilisant des métaphores conventionnelles sont également perçus comme plus ambigus, mais moins con-vaincants et émotionnellement attrayants. De plus, les participants croient davantage à la conclusion lorsque la prémisse contient une métaphore positive plutôt que néga-tive, ce qui aide plutôt l'évaluateur à détecter le sophisme.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".