DAMed If You Do; DAMed If You Donât: Cohenâs âMissed Opportunitiesâ
Bibliographic record
Abstract
In his paper, âMissed Opportunities in Argument Evaluation,â Daniel Cohen has in his sights a âcuriousâ 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 âDominant Adversarial Modelâ of argumentation â 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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".