Substantial & Punitive Costs Awards Against Self-Represented Litigants
Bibliographic record
Abstract
This report focuses on decisions that award costs against SRLs: that is, when they are on the losing side. We are interested in whether there is any difference in the way a losing party is treated, and how costs against them are assessed when they are self-representing, as opposed to when they are represented by a lawyer. At NSRLP our attention was first drawn to this issue as a result of our intervention in Pintea v Johns, where an SRL who failed to attend two case management conferences was held in contempt and ordered to pay $83,000 in costs (this order was later set aside by the Supreme Court of Canada). We have also noticed other judicial comments that suggest that substantial or punitive costs may be seen as ‘warning off’ those who are considering attending court without a lawyer. For example, such costs may be awarded against an SRL as a “sign to others who contemplate using the Court room as a personal soap box, that there will be a price to pay for doing so.”5 Another judge indicated that demonstrating disregard for the rules of the court “is simply intolerable and must be sanctioned by the court to protect the integrity of the court process and as a warning to the mother [the SRL] and other litigants that this kind of behaviour will have significant consequences.”6 While the rationale for awarding substantial or punitive costs (see below) is about deterring and/or punishing bad behaviour, SRLs are relatively new to judicial decision-making, and we wanted to begin to interrogate how their behavior in litigation was being evaluated and (in some cases) ‘punished’.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".