Self-represented Litigants in Family Law Disputes: Views of Alberta Lawyers
Bibliographic record
Abstract
The current study modified the instrument in a survey by Birnbaum & Bala (2012) with Ontario lawyers to obtain the experiences with and opinions of Albertans regarding self-representation. The Survey on Experiences with Self-represented Litigants was a web-based survey that was conducted with a sample of family law lawyers in Alberta. The sample was compiled from various lists maintained by the Canadian Research Institute for Law and the Family (CRILF) and e-mail addresses were verified by individual lawyers’ and law firms’ web sites and the 2012-13 Alberta Legal Telephone Directory. An initial invitation to complete the survey along with a link to the web site containing it was e-mailed to 174 family law lawyers across Alberta on June 13, 2012 with a request that they complete the survey by July 6, 2012. A reminder e-mail to the complete sample was sent on June 26th and the survey was closed to new responses on July 31, 2012. A total of 73 valid surveys were completed, resulting in a response rate of 42%. The survey contained background questions regarding respondents’ experience in the family law area in general, as well as their experiences with self-represented litigants in the family law area. In addition, participants were asked their views on alternatives to the traditional family law model, their opinions of parenting education workshops, and their involvement in providing pro bono services in family law.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".