Tracking\tthe Continuing Trends of the Self-Represented Litigants Phenomenon: Data from the National Self-Represented Litigants Project, 2015-2016
Bibliographic record
Abstract
From 2011-2013, Dr. Julie Macfarlane conducted a study about 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). Since the Study’s release in 2013 – “The National Self-Represented Litigants Project: Identifying and Meeting the Needs of Self-Represented Litigants” – SRLs continue to contact the National Self-Represented Litigants Project (NSRLP). This led the research team to develop an “Intake Form” in SurveyMonkey, in order to collect information from SRLs across Canada. While the data provided in the Intake Forms is less detailed and the SurveyMonkey format offers less context than the original study interviews, the questionnaire tracks SRL demographics using the same variables, such as income, education level and party status. The Intake Form also provides a glimpse into SRL personal experiences based on a final question which is “open format”. NSRLP is committed to regular reporting on this data. Our last effort spanned from March 2014-2015. This Report presents our latest data from 73 respondents (collected from April 01 2015-December 31, 2016). Additionally, in this Report, we shall compare what we see in this new data to the same variables reported in both the 2013 Research Report, and in the 2014-2015 Intake Report.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".