DAMed If You Do; DAMed If You Donât: Cohenâs âMissed Opportunitiesâ
Bibliographic record
Abstract
In his paper, “Missed Opportunities in Argument Evaluation,” Daniel Cohen has in his sights a “curious” asymmetry in how we evaluate arguments: while we criticize arguments for failing to point out obvious objections to the proposed line of reasoning, we do not consider it critically culpable to fail to take into account arguments for the position. Cohen views this omission as a missed opportunity, for which he lays the blame largely at the metaphorical feet of the “Dominant Adversarial Model” of argumentation – the DAM account. We argue here that, while Cohen criticizes the DAM account for conceptualizing arguments as essentially agonistic, he accepts its basic framing and does not follow his critique where it leads. In so doing, he misses the opportunity to develop an alternative, non-adversarial account of argumentation which would avoid his criticism of how we evaluate arguments.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".