Self-Represented Litigants & Legal Doctrines of “Vexatiousness” - An Interim Report from The National Self-Represented Litigants Project
Bibliographic record
Abstract
The Self-Represented Litigants Case Law Database Project (the “CLD” Project) is a research initiative of the National-Self-Represented Litigants Project and as such, an extension of Director Julie Macfarlane’s original 2013 Study on Self-Represented Litigants (“SRLs”). The CLD tracks emerging jurisprudence across Canada which affects SRLs. The development of the CLD was driven by the fact that no other organization in Canada was systematically tracking and analyzing case decisions on SRLs. To date, NSRLP researchers have identified more than 600 important cases that fall within our parameters (below) and over 360 Canadian decisions have been analyzed and entered into the database. As the database builds, it is possible to see trends emerging from the data.\nWhen a judge determines that a litigant’s behavior has abused the court’s processes, the litigant is designated as “vexatious”, and consequently barred from accessing the court. Courts are given the authority to designate a litigant as vexatious by using their respective rules of court, legislation, or by common law. Although not completely uniform, the elements required to find a litigant vexatious are similar across in Canada. The CLD examines those cases in which a vexatious designation has been applied to a litigant who is an SRL, or where there is other discussion of an SRL’s behaviour in terms that suggest vexatiousness, but without a formal designation as such (see “vexatiousness lite” below at section 4.
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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".