Introducing the Self-Represented Litigant Case Law Database
Bibliographic record
Abstract
The purpose of the SRL Case Law Database is to highlight patterns and themes relevant to SRLs, as evidenced by decisions reported by Canadian courts.\nJudges are now routinely being asked to consider issues that relate directly to the Access to Justice challenges of self-represented litigants. Judicial decision-making in cases involving SRLs is a new area of law, and one which presents many challenges for a traditional “strict neutrality” model of judging.\nWe hear frequently from both SRLs who contact us to share their particular case outcomes, and legal professionals (lawyers, court clerks) who send us particular decisions, in anticipation of our interest.\nWe believe that the Database will be of interest and practical use to lawyers, judges, SRLs, and the public. In 2018, we will begin to publish detailed research reports on how these cases are being decided across the country and in individual provinces. Once we have populated the Database with both family and civil cases back to 2010, we shall publish the complete SRL Case Law Database on the NSRLP website, where it will be searchable, downloadable and free.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.018 |
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".