Victim Services’ Implementation of Mobile Tracking Systems for Victims of High-Risk Gender-Based Violence Cases in Ontario
Bibliographic record
Abstract
Since 2012, Ontario Victim Services providers have been a leading force in implementing Mobile Tracking Systems, a technological device some victim advocates and law enforcement officials believe will reduce risks in gender-based violence cases. The Mobile Tracking System resembles a small pager-like device that clients carry at all times. When activated in a high-risk gender-based violence emergency, the device aims to facilitate timely law enforcement assistance by emitting a GPS tracking signal and alerting first responders to a ‘Priority 1’ call. Mobile Tracking Systems have undergone a rapid increase in attention by the media, government, service providers, and wider public as the devices are perceived to be a safety-enabling technology for gender-based violence cases. Mounting calls to fund such technologies have emerged in light of pandemic safety measures and during a 2022 Coroner’s Inquest held to investigate a triple femicide in Renfrew County, Ontario. In this Inquest, the Jury recommended that Mobile Tracking System technologies be funded by the Government of Ontario, while recently in Quebec, 41 million dollars was invested into GPS tracking technologies for gender-based violence cases. Despite gaining substantial traction in public and media discourse, Mobile Tracking Systems have been underrepresented in scholarly literature. To respond to this gap, this thesis employs qualitative methods to examine Mobile Tracking Systems in the context of gender-based violence cases in Ontario. In particular, through the examination of 91 textual documents and 10 semi-structured interviews with service providers involved in case referral and the administration of Mobile Tracking Systems, this study traces the history, development, and use of Mobile Tracking System devices in the context of gender-based violence cases in Ontario, and investigates the impact of panic button alarms on criminal justice responses to gender-based violence. To examine Mobile Tracking Systems, this thesis draws on relevant theoretical frameworks in the fields of Science and Technology Studies and critical perspectives on law and criminal justice. By tracing the development of panic button alarms to their current use in Ontario, this thesis reveals a shift toward pro-carceral safety measures that embrace technology as a perceived tool to reduce gender-based violence. As this thesis details, approaching safety work in this manner not only reflects, but also perpetuates particular assumptions about victims that pressure them to align their behaviour with the goals of the criminal legal system. The thesis argues that designing and administering a technological tool for victims of gender-based violence that centers the criminal legal system has direct impacts on victims when seeking support. The findings of this project have implications for Ontario Victim Services providers, police services in Ontario, and other agencies that support victims of gender-based violence cases, as they draw attention to how the implementation of panic button alarms as a perceived safety-enabling technology directly impact victims accessing support services for gender-based violence cases. Finally, the study’s findings can inform policy and practice related to the GPS tracking \ntechnologies in the context of mounting calls to fund panic button alarm technologies in Ontario.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".