Self-represented Litigants in Family Law Disputes: Contrasting the Views of Alberta Family Law Lawyers and Judges of the Alberta Court of Queen's Bench
Bibliographic record
Abstract
This report presents an analysis and comparison of the Bertrand et al. (2012) Survey on Experiences with Self-represented Litigants and Boyd et al. (2014) Survey on Self-represented Litigants in Family Law Matters, which used a number of common questions allowing for the direct comparison of the views of Alberta lawyers and those of Court of Queen’s Bench judges. The Survey on Experiences with Self-represented Litigants was a web-based survey that was conducted with a sample of 73 family law lawyers in Alberta in June and July of 2012. The Survey on Self-represented Litigants in Family Law Matters was conducted with a sample of 32 judges attending the Alberta Court of Queen’s Bench education seminar held in Calgary, Alberta from 29 to 31 January 2014. Both surveys asked questions regarding judges’ and lawyers’ experience with family law in general, their perceptions of and experiences with self-represented litigants in family law disputes and their opinions about the effects of self-represented litigants on case outcomes. Participants were also asked for their views on alternatives to the traditional start to finish model of legal representation in family law matters, such as the retainer of counsel for limited purposes and the delegation of certain services normally performed by lawyers to paralegals. The results of these surveys should not be taken as representative of the views of all family law lawyers in Alberta or all judges of the Alberta Court of Queen’s Bench.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".