Bibliographic record
Abstract
Abstract This chapter examines a sentencing concept that Julian V Roberts has addressed in a large number of publications in the past forty years or so: proportionality. It first examines the efforts that Roberts and others have made to try to find mechanisms to give weight to an offender’s criminal record in a manner that does not undermine proportionality. Second, the chapter examines the Canadian ‘conditional sentence of imprisonment’ (a version of the suspended sentence) suggesting that its inadequate legislative framing in Canada reflected the fact that its real purpose was only to reduce the use of imprisonment. In both of these areas, any attempt to come up with clever ways of maintaining the pretence that sentences are proportional may be futile. For example, since criminal records vary on a large number of dimensions, some of which will affect the manner in which a sentence is served, it may be impossible, at sentencing, to go beyond a general statement related to the importance of proportionality and an indication that sentences can be affected by criminal records. Similarly, a broad statement saying that all sentences—custodial, suspended, and community—should be roughly proportionate to the severity of the offence may be all that can be meaningfully stated.
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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".