In view of an express regulation: Considering the scope and soundness of a contrario reasoning
Bibliographic record
Abstract
A contrario reasoning (or ‘a contrario argument’ or ‘argument a contrario’) is traditionally understood as an appeal to the deliberate silence of the legislator: because a legal rule does not mention case X specifically, the rule is not applicable to it. Modern perspectives on legal reasoning often apply this label to a broader concept of reasoning, namely the reasoning by which a legal rule is not applied because of the differences between the case at hand and the one(s) mentioned in the legal rule. This article first explains how the broader concept could have come into being, and then argues that from an argumentation theoretical point of view the modern concept makes no sense as a category of argumentation. Furthermore it is shown under which conditions the traditional concept can be sound.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".