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Record W7070889566

Reasonable Bail or Bail at All Costs? Exploring the Role of Defence Lawyers in Bail Preparations and Negotiations

2022· dissertation· en· W7070889566 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2022
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationEconomic JusticePower (physics)Work (physics)Face (sociological concept)
DOInot available

Abstract

fetched live from OpenAlex

Accused persons have a constitutional right to reasonable bail, but they regularly face delays to release and excessive bail conditions. Bail decisions are largely shaped by private, out-of-court negotiations between Crown attorneys and defence lawyers. The accused relies on the professional expertise of defence lawyers to navigate bail negotiations and get them the best outcome possible. However, research has not yet explored how defence lawyers prepare for and negotiate bail. The current study utilizes data from 18 semi-structured interviews with Ontario-based defence lawyers. The findings show that while defence lawyers care about and argue for reasonable bail terms, their ultimate goal is to obtain a release for their client, regardless of whether the terms of release are the least restrictive according to relevant legal standards. Defence lawyers also place high importance on collecting and strategically using information about the case and the Crowns and justices they work with. Additionally, they often behave in a risk-averse and cooperative manner because disagreeing with Crown requests and arguing matters before a justice risks delaying or destroying the client’s release. Crown and justice discretionary power over the defence means that policy solutions that seek to protect accused persons’ right to reasonable bail must focus on altering Crown and justice behaviour.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.035
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.012
Scholarly communication0.0090.007
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.033
GPT teacher head0.210
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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