Bibliographic record
Abstract
Community legal clinics offer important perspectives on access to justice because of their proximity to, and relationships with, the people and communities who are so often the subject of the debate. In this article, I focus on three broad and interrelated insights about access to justice that I have gained as a result of my work at Community Legal Assistance Services for Saskatoon Inner City (CLASSIC), and through my reading of the “community lawyering” scholarship. First, from a community legal clinic perspective, access to justice cannot be understood “out of context”. That is, close attention to historical, economic, political, and social context is a crucial part of grappling with the problem of access to justice. Second, a community legal clinic perspective reveals that an engaged consideration of “community” is foundational to any conception of access to justice. Third, the struggle for access to justice demands “long-haul” commitment by advocates at community legal clinics. Overall, these insights reveal the problem of access to justice as only one thread in a complicated web of social injustice, impossible to untangle without addressing the larger web. These insights also tend to unsettle dominant visions of access to justice, which often focus on access to formal dispute resolution institutions and to disassociate access to justice from other social, economic and political problems. Ultimately, these insights have implications for the ways in which lawyers working in the community understand their place in broader struggles for justice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.027 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 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".