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Record W2318371545

Section 276 Misconstrued: The Failure to Properly Interpret and Apply Canada's Rape Shield Provisions

2016· article· en· W2318371545 on OpenAlexaffabout
Elaine Craig

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSection (typography)ShieldPolitical scienceForensic engineeringLawBusinessEngineeringGeologyAdvertisingPetrology
DOInot available

Abstract

fetched live from OpenAlex

Despite the vintage of Canada’s rape shield provisions (which in their current manifestation have been in force since 1992), some trial judges continue to misinterpret and/or misapply the Criminal Code provisions limiting the use of evidence of a sexual assault complainant’s other sexual activity. These errors seem to flow from a combination of factors including a general misunderstanding on the part of some trial judges as to what section 276 requires and a failure on the part of some trial judges to properly identify, and fully remove, problematic assumptions about sex and gender from their analytical approach to the use of this type of evidence. A lack of clarity as to how section 276 works, and the ongoing reliance on outdated stereotypes about sexual assault to interpret the provisions, are particularly problematic because trial judges continue to face applications to adduce evidence of a complainant’s sexuality activity which are inflammatory, discriminatory, and clearly excluded by section 276 of the Criminal Code. The reality that some defence counsel continue to ignore, or attempt to undermine, the legal rules dictated by section 276 heightens the need for competence, rigor, and accuracy among trial judges tasked with the adjudication of these applications. Following a brief explanation of how Canada’s rape shield regime works, four types of problems with the interpretation and application of section 276 are identified using examples from recent cases.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.959

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 studies0.0010.000
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 head0.008
GPT teacher head0.256
Teacher spread0.248 · 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 designNot applicable
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

Citations2
Published2016
Admission routes2
Has abstractyes

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