From Serving the Needs of the Few to Serving the Needs of the Many
Bibliographic record
Abstract
From the outset, the objective of the Rural Mobile Law Van project has been to expand service in underserved rural areas, first to rural Wellington County in the summer 2019 pilot project and then in the second three-year phase of the project from 2021 to 2024 to Wellington County and to the adjacent North Halton area as well. The mobile law van operates between May and the end of October. During the fall and winter when Canadian weather becomes too inclement for an outdoor service the winter “law van” moves to various indoor venues in the same towns where the Law Van visits in the summer. The summer van identifies unmet legal need by going out to where people live or spend much of their time, maximizing accessibility mainly by being highly visible in small towns throughout the area. The summer Law Van allows people to come to a high visibility location where it is parked for the day, where they can request free legal help in their own community, at a convenient time, and on their own terms. In this small rural area, the project is solving a big problem that was occurring in rural Wellington County and North Halton. At the same time the Law Van project is developing an approach that is addressing a big problem that has for a long time been a feature of legal aid. The Law Van has, within the confines of a small space and a short time frame, turned back a problem that has existed for legal aid generally since the beginning, a problem that has been becoming more pronounced in legal aid everywhere over time. That problem is the rationing of services to too few people. Although not an explicit objective from the outset, one way to understand what the Law Van is accomplishing is that it represents a way to serve the needs of the many rather than the needs of the few.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.041 | 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".