Voices and Visions of the Future: The Stories of Self-Represented Litigants who are Survivors of Intimate Partner Violence
Bibliographic record
Abstract
This paper will focus on SRLs who are survivors of IPV and their experiences in family court in Ontario. Section 2 will begin by providing a background about IPV and SRLs in Ontario, narrowing down the focus of this paper to those in family court while explaining alternatives in criminal court. Section 3 will focus on the Woman, centring the experiences of survivors who self-represent. Who they are and why they self-represent are crucial questions to answer before making recommendations on how to improve their situation. Section 4, entitled the Court, will explore the actual court process and how difficult it can be for survivors. Drawing primarily from interviews, this section will highlight the many issues raised with court procedure including inefficiency, retraumatization, and perception. Section 5 moves into the Reality, looking at the long lasting impact that self-representation combined with IPV has on survivors. These take the form of social, psychological and physical effects. Finally, Section 6 of this paper will conclude with policy recommendations directed towards the Ontario and Superior Court of Justice on how to improve the justice system when it comes to SRLs who are survivors of IPV. Stemming from research and interviews, these recommendations will encompass all of the issues brought to our attention by survivors and provide practical suggestions on how to improve their experiences moving forward. Many recommendations will also be directed towards the officials in court as well like lawyers, judges, jury, social workers, and others who interact with these women throughout the process.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.025 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".