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Record W7029279616

On the Internet, Nobody Knows You are a Dog: Contested Authorship of Digital Evidence in Cases of Gender-based Violence

2022· article· en· W7029279616 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHomicide, Infanticide, and Child Abuse
Canadian institutionsnot available
Fundersnot available
KeywordsAdversarial systemCriminal justiceRelation (database)Digital evidenceDutyDigital forensicsEconomic Justicenobody
DOInot available

Abstract

fetched live from OpenAlex

We examine various aspects of digital evidence at GBV trials, drawing on relevant Canadian criminal case law. First, we describe some of the unique challenges related to electronic documents generally with respect to determining authorship. Second, we review some of the historical and ongoing practices within the criminal justice system that rely on harmful gendered myths about GBV and note the potential for these myths to emerge in relation to digital evidence. Third, we discuss the duty of investigating police officers to gather the necessary available digital evidence to demonstrate authorship and note potential gaps in current investigatory practices that could negatively impact the trial outcome for victims of GBV. Fourth, we review some of the evidentiary rules for admitting and authenticating digital evidence at trial, discussing how these rules have been interpreted and applied in the GBV context. Fifth, we examine what evidentiary burdens the Crown faces in proving authorship at trial, highlighting the developing nature of law in this area. Finally, we conclude with several recommendations for various justice system actors on how to manage digital evidence in GBV cases where authorship may be contested.\nWhile the focus of this article is on examining and making recommendations in relation to the use of digital evidence in GBV criminal trials, we recognize significant systemic problems with the criminal justice system that make it an undesirable and unrealistic option for many victims of GBV. Victims are not always believed by the police even when they have legitimate claims, and many ongoing practices within the adversarial trial process create additional trauma for some GBV victims. It is well documented that many Indigenous and Black individuals, people of colour, and members of the LGBTQ2s+ community have experienced discrimination when engaging with the police and justice system, and as such members of these groups may be particularly disinclined to rely on the criminal justice system to address violence against them. In Canada, there is a long history of the justice system ignoring reports of violence against Indigenous women and girls, and in some cases police and other justice system actors have directly perpetrated this violence, leaving deep-seated distrust in the criminal justice system and a desire for alternative options for addressing GBV. As a result, many advocates and victims have been exploring alternative methods of justice and calling for the transformation or even abolition of criminal justice- based systems. Many victims of GBV choose not to engage with the criminal justice system, and others continue to face discriminatory systemic barriers in accessing justice within that system. Within the context of these varied and valid critiques, the recommendations in this paper are premised on the notion that, so long as the criminal justice system remains the primary state-supported mechanism for dealing with GBV, it must be accessible to all victims, all of whom are entitled to investigatory and trial processes that are fair, treat them with dignity, and do not rely on discriminatory beliefs.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.306
Teacher spread0.244 · 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 designObservational
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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