On the Contrary: Inferential Analysis and Ontological Assumptions of the A Contrario Argument
Bibliographic record
Abstract
We remark that the A Contrario Argument is an ambiguous technique of justification of judicial decisions. We distinguish two uses and versions of it, strong and weak, taking as example the normative sentence “Underprivileged citizens are permitted to apply for State benefit”. According to the strong version, only underprivileged citizens are permitted to apply for State benefit, so stateless persons are not. According to the weak, the law does not regulate the position of underprivileged stateless persons in this respect. We propose an inferential analysis of the two uses along the lines of the scorekeeping practice as described by Robert Brandom, and try to point out what are the ontological assumptions of the two. We conclude that the strong version is justified if and only if there is a relevant incompatibility between the regulated subject and the present case.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| 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".