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Record W2341045857

'Whack' No More: Infusing Equality into the Ethics of Defence Lawyering in Sexual Assault Cases

2015· article· en· W2341045857 on OpenAlexaffabout
David M Tanovich

Bibliographic record

VenueSSRN Electronic Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPlaintiffLawCivilityContext (archaeology)Economic JusticeSociologyDenialPolitical scienceSupreme courtCriminologyPsychologyHistory
DOInot available

Abstract

fetched live from OpenAlex

In 1988, defence lawyers in Ottawa were instructed to “whack” the complainant in sexual assault cases. These were their marching orders:“[W]hack the complainant hard” at the preliminary inquiry...“Generally, if you destroy the complainant in a prosecution...you destroy the head. You cut off the head of the Crown’s case and the case is dead...[A]nd you’ve got to attack the complainant with all you’ve got so that he or she will say [‘]I’m not coming back in front of 12 good citizens to repeat this bullshit story that I’ve just told the judge.[’]”The “whacking” continues. This defence culture explains, in part, why defence lawyers have no hesitation in leaving their ethics at the courtroom door so as to exploit and perpetuate stereotypes about women and sexual assault in defence of their clients. With the recent focus on civility by the legal profession, and concerns raised about the failure of law reform initiatives to improve reporting and the fair prosecution of sexual assault cases, it is time to address the discriminatory lawyering and denial of access to justice that is taking place in these cases. The article begins by exploring how sexual assault is different from other offences in terms of how it is processed, conceived of, and defended by lawyers. It is argued that this difference requires a rethinking of ethical lawyering in this context. The next part attempts to set out a normative framework that is largely grounded in legal and ethical norms including equality values, the lawyer’s duty to not discriminate, as well as an advocate’s obligation to act in “good faith” and not mislead the court. The article turns to applying this framework by setting out what defence tactics should be ethically barred, particularly when you know your client is guilty. The critical question of when you know your client is guilty is also addressed. The final part uses three leading Supreme Court of Canada evidence cases (R v Khan; R v Osolin; R v Parrott) to examine how the proposed ethical limits might have impacted the conduct of the defence.

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)0.020
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.004
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 head0.131
GPT teacher head0.454
Teacher spread0.323 · 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.

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

Citations6
Published2015
Admission routes2
Has abstractyes

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