Tracking the Trends of the Self-Represented Litigant Phenomenon: Data from the National Self-Represented Litigants Project, 2018/2019
Bibliographic record
Abstract
From 2011-2013, Dr. Julie Macfarlane studied the experiences of self-representation in Canada in three provinces: Ontario, British Columbia, and Alberta. She conducted detailed personal interviews and/or focus group interviews with 259 self-represented litigants (SRLs). After the publication of Dr. Macfarlane’s initial report in 2013, SRLs continued to contact the National Self-Represented Litigants Project (NSRLP). This led the research team to develop an “Intake Form” in SurveyMonkey, in order to continue to collect information from SRLs across Canada. While the data provided from the replies to the Intake Form is less detailed than the original study interviews, the questionnaire tracks SRL demographics using some of the same variables, such as income, education level and party status. It also asks questions about the SRL’s experience with prior legal services, mediation services, and bringing a support person to court. The Intake Form also provides a glimpse into SRL personal experiences based on a final question which is “open format”. NSRLP is committed to continued reporting on the SRL phenomenon. Our last report on intake data spanned from April 1, 2015-December 31, 2016, and included data from 73 respondents. This latest Report presents data from 66 respondents, collected from January 1, 2017 to December 31, 2017.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".