Flourishing: A plan to strengthen public legal education and information, the BC PLEI Ecosystem Project
Bibliographic record
Abstract
The Public Legal Education and Information (PLEI) Sectoral Planning Project, headed by Dr. Catherine Dauvergne, K.C., was commissioned by the Law Foundation of British Columbia with the goal of making recommendations about how to improve public legal education and information in the province. The project was co-sponsored by the Law Foundation and the province’s Ministry of the Attorney General. We have come to understand this constellation of resources and organizations as the “public legal education and information ecosystem.” There is a wide array of high-quality, easily accessible, clearly written, legal information available in British Columbia. Ecosystem leaders are at the forefront of innovation, and are deeply committed to the communities they serve. Other leaders across Canada have a deep respect for the work done in this province. However, people struggle to locate and understand publicly available legal information. Frontline legal service providers, who often help people understand this information, are stretched very thin and often struggle themselves with highly pressured work and inability to meet their clients’ needs. For those creating and delivering PLEI, a desire for strengthened collaboration and cooperation is strong. This report presents the findings from our research and the 30 recommendations that we believe can address many of the challenges we identified.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".