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Record W3123144515

Deferred prosecution agreements in England and Wales:: castles made of sand?

2019· article· en· W3123144515 on OpenAlexaboutno aff
Colin King, Nicholas Lord

Bibliographic record

VenueResearch Explorer (The University of Manchester) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsLawArchaeologyPolitical scienceHistory
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.118
GPT teacher head0.332
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

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