Virtual Justice?: An Analysis of Access to Court for People Experiencing Homelessness
Bibliographic record
Abstract
Homelessness in Canada remains a wicked social problem that often intersects with compounding forms of marginalization. The criminalization of homelessness and living life in the public sphere explain, in part, why this population is likely to interact with the criminal justice system. Following the onset of the pandemic, the courts were forced to modernize and embrace digital technologies to maintain operations. Now four years since these changes, there are no signs of turning back and the court system is continuing forward with a hybrid model. Despite this, there is minimal research on the impact of virtual court proceedings on people experiencing homelessness. This thesis fills the gap in the scholarship through an analysis of virtual court proceedings to identify barriers and facilitators to accessing justice for unhoused people. Building on 18 interviews with professionals who assist unhoused clients navigate court, I argue that the court system, both in-person and virtual, has never worked for this population. While virtual court and hybrid proceedings have the ability to improve accessibility, structural inequities experienced by unhoused people often require workarounds from service providers to make this system work. These accessibility challenges are overshadowed by the court’s efforts to enforce outdated hierarchical symbols of power and notions of legitimacy in a virtual setting. Further, I argue that the spatial and temporal realities of people experiencing homelessness conflict with the structure of court. As a result, there are a number of barriers this population must overcome in order to meaningfully participate in court. The findings of this research emphasize a need to address systemic forms of marginalization before justice can truly be achieved.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".