`Neutrality`, `Choice`, and `Ownership` in the Construction, Use, and Adaptation of Judicial Decision Support Systems
Bibliographic record
Abstract
This article examines the character and future of Judicial Decision Support Systems (JDSS's) in relation to the activity of judicial sentencing. There are many varieties of JDSS which could be applied to sentencing. However, in terms of attracting judicial and political commitment 'Sentencing Information Systems' seem to be emerging as the predominant JDSS model. This model stresses values of data neutrality; judicial choice; and, judicial ownership of sentencing practice and sentencing reform. The article proceeds to examine the 'flip side' of each of these values. It discusses the reasons for the apparent neutrality of SIS data arguing that this 'neutrality' is necessarily a construction based in sentencing research. Examining the value of judicial choice in whether or not the system should be consulted, the article presents results of evaluation of the extent and nature of use of the Scottish Sentencing Information System currently being operated by High Court judges. There is some reason to believe that previous Canadian experience may not necessarily be replicated elsewhere, although it is still early in the history of the Scottish project. Finally, the article considers the ability to retain judicial ownership of the system and public access arguing
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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 | 0.085 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".