Exploring Public Attitudes to Sentencing Factors in England and Wales
Bibliographic record
Abstract
Our volume now moves away from the theory and practice of mitigation and aggravation at sentencing to consider the views of the community. The next chapter, by Austin Lovegrove, reports findings from a project involving members of the public and judges in Australia. In this chapter we draw on a number of large-scale quantitative surveys to explore public attitudes to the factors that aggravate or mitigate sentence. The purpose of this essay is to describe recent research findings which illuminate public attitudes to a number of common sentencing factors. These results challenge the view that the public are inflexible, punitive sentencers with little interest in mitigation, and shed light on the model of sentencing to which many people subscribe. CHAPTER OVERVIEW The chapter begins by discussing some recent survey trends with respect to public attitudes to sentencing. We then discuss some reasons why we might want to know about attitudes to mitigation and aggravation. Some methodological caveats are issued; different approaches to measuring public attitudes will yield very different responses. This discussion is followed by a presentation of some specific research findings from a study involving a large, representative sample of the public in England and Wales. Finally, we draw some conclusions for the sentencing process.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".