Bibliographic record
Abstract
In Furman v. Georgia, the United States Supreme Court announced that it would not tolerate a capital sentencing regime that imposed death sentences in a seriously arbitrary fashion. The question I ask in this paper is whether we should in fact object to arbitrariness in punishment. The answer I propose is that under plausibly adverse conditions, we might not object to arbitrary penal outcomes, because under those conditions a fair distribution of punishment would be one that equalizes chances across a class of similarly situated criminals. In particular, fairness may require no more than a rough equalization of ex ante chances under conditions of resource scarcity, an inability to rank claims reliably by comparative desert, and a pressing need for punishment to be imposed. I call this an ex ante theory of fairness. The central virtue of ex ante fairness is that it is capable of reconciling parsimony in punishment with equity in its distribution, even when claims about who deserves what are deeply contested. Adopting an ex ante standard of fairness means that a concern for fair treatment of the guilty need not blind us to the realities of the severe resource constraints faced by American criminal justice, and vice versa. After laying out the argument for ex ante fairness in general terms, I proceed to show how several prominent features of American criminal law and procedure—the Supreme Court’s capital jurisprudence, prosecutorial discretion, judicial sentencing discretion, and “strict” criminal liability—all exhibit an implicit commitment to an equalization of chances rather than of outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".