Bibliographic record
Abstract
To celebrate the 5-year anniversary of the National Self-Represented Litigants Project (NSRLP), we decided to go back to where it all began.\nThe NSRLP was established following recommendations made at Opening the Dialogue: The SRL Phenomenon, an event following the release of Dr. Julie Macfarlane’s National Self-Represented Litigants (SRLs) Research Study. The 2013 event was a small, invitation-only stakeholder dialogue including self-represented litigants (SRLs), lawyers, policymakers, judges and academics, representing many different experiences within the legal system.\n“Continuing the Dialogue” – held October 11 – 13 2018 at Windsor Law – adopted the same format, 5 years on. Attendees were invited from almost every Canadian province. Over the course of the event, 15 SRLs and 45 justice system representatives took part in facilitated plenary discussions, small working group discussions focused on specific issues related to SRLs and the justice system, listened to panels presented by both SRLs and justice system insiders, and attended small networking events.\nFor the complete attendees list see Appendix A; for the event agenda see Appendix B. An opening evening reception brought the attendees together for an initial meet-and-greet and a few hours of relaxation before getting down to work the following morning.
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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.042 | 0.016 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.018 | 0.037 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".