He old people are the song, and we are their echo: resurgence of w sne law and legal theory
Bibliographic record
Abstract
This dissertation attends to pressing questions of strategy and tactics in relation to Indigenous law revitalization in the context of the climate crisis. Grounded in my own WSNE legal order, I provide an accounting of the context in which the resurgence of WSNE law is occurring, and clarity regarding what we hope to accomplish with the revitalization of WSNE (and more broadly, Indigenous) law, both locally and in response to global climate crisis. Doing so prompts questioning of the very foundations of Canadian constitutionalism, and indeed, our most basic ideologies and conceptualizations of our place and relationships within the world. From a position that our theory and methodology of Indigenous law revitalization, and our diagnosis and approaches to the climate crisis must be intricately entwined and mutually reflective, the dissertation sets out to argue that nothing short of a fundamental reimagining of our relationships within the world, and thus the social, legal, political, and economic structures those relational understandings condition, is required. Approaching such encompassing questions requires the creation of conversations across fields such as Indigenous law, critical Indigenous studies, and political ecology.\nGuided by WSNE law and the responsibilities and obligations it entails, how might we navigate ongoing dynamics of settler colonialism and climate crisis? While some form of decolonized relationship with Canada seems necessary but insufficient within the context of a global climate crisis, what would actually be required to meaningfully respond and reimagine healthy relationships between all beings? Will forms of state-delegated authority, such as co-management agreements, or forms of green capitalism, such as market measures or carbon offsets for Indigenous-led conservation, ever suffice to address our longstanding, and yet continually unfolding, predicaments of settler colonialism and climate crisis? As I argue for a more foundational re-imagining of our relationships and place within the world, my analysis also reveals that we are not left groundless in this re-imagining as we can draw upon the wisdom and practices housed within our respective Indigenous traditions.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.087 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".