Bibliographic record
Abstract
The paper calls for argumentation theory to learn from moral and political philosophy. Several thinkers in these fields help understand the occurrence of what we may call legitimate dissensus: enduring disagreement even between reasonable people arguing reasonably. It inevitably occurs over practical issues, e.g., issues of action rather than truth, because there will normally be legitimate arguments on both sides, and these will be incommensurable, i.e., they cannot be objectively weighed against each other. Accordingly, ‘inference,’ ‘validity,’ and ‘sufficiency’ are inapplicable notions. Further distinctive features of pro and con arguments in practical argumentation are explored, and some corollaries are drawn regarding evaluative norms of legitimate dissensus. Examples from immigrationrelated public debates in Denmark are given.
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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 | 0.060 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".