Bibliographic record
Abstract
This article analyzes an innovative community-based educational project called the "Wahkohtowin class" in Saskatoon, Saskatchewan. The class brings together former gang members, Indigenous high-school students and university students from the disciplines of law, English, and Indigenous studies to learn together about law, justice, and injustice. Students in the class read legal texts together and then discuss and critique these texts in the context of the lived experiences of people in the class. Drawing on the experience of the Wahkohtowin project, this article argues that the practice of lawyers and law students reading and interpreting legal texts and talking about justice together with members of marginalized communities is an "access-to-justice innovation." It is an innovation because it is a model that positions lawyers and law students not as experts but, rather, as co-learners and co-creators of knowledge with people who possess important lived experiences of the impacts of law and the justice system. It resists the notion that the legal system or lawyers possess a monopoly on justice, opening space for lawyers, law students, and community members to imagine justice together. Overall, this article argues that it is important for those within the legal system who are seeking to improve access to justice to engage with, and learn from, members of marginalized communities who have direct experience with the justice system and that the Wahkohtowin class is one example of how this can happen.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.023 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".