Deferred prosecution agreements in England and Wales:: castles made of sand?
Bibliographic record
Abstract
Negotiated settlements are increasingly regarded as an alternative tool against corporate criminality, with numerous countries now embracing such settlements. In England and Wales, amidst concerns relating to corporate criminal liability, the government introduced deferred prosecution agreements (DPAs) in 2014. A DPA has been described as ‘a bargain under which the prosecutor undertakes not to proceed with the prosecution of a corporation for a fixed time in return for the defendant mending its ways and paying a financial penalty for the privilege.’ Similar powers are well established in some jurisdictions, particularly the US, and they have recently been introduced elsewhere too. For example, France introduced equivalent powers in 2016, namely the Judicial Convention of Public Interest. In 2019, French authorities issued Guidelines on this power, which are influenced by, inter alia, experiences from England and Wales. In 2018, both Singapore and Canada introduced DPAs directly influenced by experiences in England and Wales. Other countries are considering introducing DPAs, including Ireland. The Irish Law Reform Commission has proposed the introduction of DPAs based on the regime in England and Wales, rather than that in the US. Thus, five years on from their introduction in England and Wales, it is timely to re-examine the DPA regime, not least given its influence on developments in other jurisdictions.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".