LAWYERS’ PERCEPTIONS OF THE FAIRNESS OF JUDICIAL ASSISTANCE TO SELF-REPRESENTED LITIGANTS
Bibliographic record
Abstract
How much assistance should a trial judge provide a self-represented litigant [SRL] before the judge’s impartiality will be reasonably questioned? This question has been of continuing concern to both the bench and bar ever since the rise of the pro se litigation movement in the late 1990s, particularly in the context of “mixed” cases involving an SRL and a represented party. Case law and ethics codes provide inconsistent decisions and vague guidelines for judges, who must balance their duty to provide reasonable assistance with their duty to ensure a fair trial for all parties. This paper reports the results of a survey administered to 210 Canadian family law practitioners who were presented with 16 hypothetical scenarios involving an SRL and a represented party. Respondents indicated their views regarding the impartiality and helpfulness of the trial judge in each scenario, involving various procedural defaults by the SRL and different forms of judicial assistance or lack thereof. The results indicate that lawyers' perceptions of a judge's impartiality are affected, inter alia, by the favourability of the outcome for the SRL, and whether the assistance provided dealt with procedural or substantive matters. Future research is needed to determine whether a consensus can be established regarding perceptions of lawyers, lay persons, and judges regarding which forms of assistance are reasonable and required, permissible, or impermissible.
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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 | 0.023 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".