'Necessity knows no law': On extreme cases and uncodifiable necessities
Bibliographic record
Abstract
This article analyses the category of extreme cases - cases involving catastrophic consequences the avoiding of which requires severe measures (e.g. torture, shooting a plane in 9/11 situations, etc). We first reject two traditional solutions to extreme cases: deontology/threshold deontology (as traditionally understood) and consequentialist solutions. Our proposal maintains that what is most pernicious is not the violation of moral rules as such but their principled or rule-governed violation. Maintaining a normative distinction between acts performed under the direction of principles/rules, on the one hand, and unprincipled, context-generated acts, acts performed under the force of circumstances, on the other, allows for accommodating the necessity of infringements in extreme cases within a (non-conventional) deontological framework. Agents who perform acts under extreme cases ought not to be guided by rules or principles. Instead, they ought to make particular judgments not governed by rules. We also establish that this solution follows from the Kantian conception of human dignity.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".