Building the Aboriginal Conference Settlement Suite: Hope and Realism in Law as a Tool for Social Change
Bibliographic record
Abstract
In 2014, the provincial government unveiled a new courthouse in Thunder Bay, Ontario, featuring a conference area designed to emulate an Anishinaabe roundhouse. The “Aboriginal Conference Settlement Suite” epitomizes efforts to support Indigenous justice within the criminal justice system. However, despite similar efforts in the past, the circumstances of Indigenous peoples in Canada have not improved. This ongoing commitment to legal solutions is emblematic of mainstream views of law as a problem-solving instrument. Notwithstanding awareness of its failings, law reformers remain dedicated to using law as a tool for social change. Employing a case study method focusing on the new courthouse, I challenge a prevailing wisdom that law reform outputs are manageable and in our control. I argue that similar to a courthouse, which is a concrete, physical structure as well as a symbol of justice, so too is the legal instrument both material and metaphorical, with concrete outcomes and symbolic forms. While treating law as a literal tool may give law reformers a longed-for sense of mastery, this approach belies law’s diffuse constitutive power and the various paradoxes in reformers’ actions. Accepting law’s dual nature is essential for candid and accurate assessments about the possibilities and limits of change through law.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.042 | 0.115 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| 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".