Canada's criminal justice response to technology-facilitated intimate partner violence
Bibliographic record
Abstract
This dissertation critically examines the Canadian criminal justice response to technology-facilitated intimate partner violence (TFIPV) through an examination of relevant jurisprudence from 2016-2019. TFIPV involves an individual’s abuse of digital technology or digital media in committing violence against their partner, and includes technology-facilitated coercive control (TFCC) behaviours, consistent with feminist scholarship. The unique features of the permanence of online data, the ability to easily publish and amplify content online, the massive amounts of data produced through our interactions with technology, and the anonymity of much online communication, each contribute to TFIPV representing both an extension of earlier forms of intimate partner violence, and a novel phenomenon requiring tailored legal and regulatory approaches. This dissertation contributes to the existing research on TFIPV and TFCC by examining how these phenomena are playing out in Canada through a comprehensive examination and feminist analysis of Canadian criminal case law involving TFIPV over four years. Quantitative analysis of the 168 cases in the data set indicate that TFIPV is a highly gendered phenomenon, and often involves monitoring, restricting, and harassing behaviours, consistent with intimate partner violence more generally. A qualitative analysis of a sub-set of 100 of the data set cases involving TFCC explores the meaning imbued by perpetrators, victims, and judges in relation to TFIPV behaviours, and identifies gaps and shortcomings in the existing criminal justice response to this violence from a feminist perspective. The majority of issues stem from failures within the criminal justice system to fully account for the gendered nature of this violence, or the evolving realities of online digital communications. Solutions to address these shortcomings form the basis of the author’s recommendations in the concluding chapter, including substantive and procedural reforms to the Criminal Code, recommendations for equality-enhancing and technology-informed judicial approaches to these offences, and policy reforms aimed at increasing criminal justice system actors’ awareness of and responsiveness to these offences.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.036 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".