Commentary on "Prosecutions, Politics and the Public Interest: Some Recent Developments in the United Kingdom, Canada and Elsewhere"
Bibliographic record
Abstract
Professor Stenning is to be congratulated for providing a fresh and timely perspective on some crucial dilemmas of prosecutorial decision-making, and for grounding his incisive analysis in a close discussion of a particularly provocative case emerging from the U.K. House of Lords in 2008. The core conundrum he addresses in his paper is the long-standing one of what should be the contours of the role played by a jurisdiction's Attorney General in prosecutorial decision-making. The context here is one in which attorneys general have multiple and significant responsibilities in governmental arenas. Specifically, he poses two questions about the Attorney General's role. The first is (i) are we close to achieving "institutional arrangements and constitutional conventions and practices" which will guarantee "a satisfactory balance between political independence and political accountability of those with ultimate responsibility for prosecutorial decision-making?". The second question is (ii) 'are we closer to achieving consensus about what such a 'satisfactory balance' might be?" The article ultimately answers both of these questions in the negative.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.046 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".