The Grim Parade: Supreme Court of Canada Self-Represented Appellants in 2017
Bibliographic record
Abstract
Self-Represented Litigants [SRLs] are persons who appear in legal proceedings without a lawyer. This study is a document- and court record-based quantitative, statistically valid profile of 122 SRLs who filed 125 leave to appeal applications in the Supreme Court of Canada [SCC] in 2017. Male SRLs outnumbered female SRLs almost 3:1. Most SRLs focused on their perceived rights, and did not engage Canadian law. Instead, most study SRLs claimed lower court judges were biased, or engaged in illegal or criminal conduct. Over a third of the study SRLs filed two or more SCC leave to appeal applications over their lifetime. One filed 19 applications, all unsuccessful. Nearly one in four study SRLs were subject to court access restrictions, an extreme form of litigation management. Problematic litigation activity was associated with repeated SCC appearances. Only a small number of study SRLs self-identified or were identified by a court as having mental health issues, but nearly one quarter of SRLs’ litigation records exhibited an atypical pattern of expanding litigation identified by mental health professionals as a characteristic of querulous paranoia. This investigation successfully developed a profile of the 2017 SCC leave to appeal SRL population and their litigation activity, and provides a model for future parallel investigations. This population is very unlikely to be representative of Canadians SRLs as a whole, but it represents a comparator, and identifies characteristics that are potentially useful to understand what occurs in other Canadian appeal courts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".