The Sentencing Theory Debate: Convergence in Outcomes, Divergence in Reasoning
Bibliographic record
Abstract
This essay is inspired by the publication of Andrew von Hirsch and Andrew Ashworth's new book, Proportionate Sentencing: Exploring the Principles. It is the latest installment in a debate that has gone on now for over thirty years between their account of sentencing and its main rival, first set out by Norval Morris and subsequently endorsed and developed by a number of other prominent sentencing writers in the United States such as Michael Tonry, Richard Frase and Kevin Reitz. This debate has heated up recently with the publication of the American Law Institute's draft Model Penal Code sentencing provisions, which explicitly endorse limiting retributivism rather than von Hirsch and Ashworth's just deserts account. We suspect that one of the tacit aims of von Hirsch and Ashworth's new book is to show that the ALI's decision was mistaken. In this essay, we argue that although both sides in the sentencing theory debate purport to provide over-arching accounts of the law of sentencing, in fact each of them concentrates its efforts on a particular aspect of the enterprise. When advocates of these two accounts are faced with real, live sentencing situations, they tend to favour broadly similar outcomes in most cases. The most striking example of this phenomenon is the endorsement of the Minnesota guidelines system by the major advocates of both accounts. Limiting retributivism has always been concerned primarily with criminal justice policy-making and institutional design. Morris, Tonry, Frase and Reitz focus much of their energy on questions such as the composition of a sentencing commission, its procedures for setting overall penalty levels, designing sentencing alternatives to prison and probation and - as a question of institutional design - the regulation of judicial discretion in sentencing. It is not surprising that the American Law Institute's new draft Model Penal Code sentencing provisions, which are focused squarely on those same questions of overall criminal justice policy and institutional design, should endorse this approach. Von Hirsch and Ashworth's just deserts account, although it does not entirely ignore questions of policy and institutional design, is primarily concerned with the justification of state coercion through the mechanisms of sentencing. For those who are concerned with the special role of the sentencing court as a forum of principle, von Hirsch and Ashworth provide the best account available - the most thorough, the most thoughtful and the most rigorous.
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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 | 0.093 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.015 | 0.019 |
| 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".