CHAPTER 1 Mino-Mnaamodzawin Achieving Indigenous Environmental Justice in Canada
Bibliographic record
Abstract
To think that Indigenous concepts of justice do not exist is Eurocentric thought." -Wenona Victor Environmental justice (EJ) has several definitions but can generally be thought of as the equitable distribution of environmental burdens and benefits across racial, ethnic, and economic groups.Despite well-documented cases of environmental injustice in Canada, particularly involving Indigenous peoples (Agyeman et al. 2009;Dhillon and Young 2010;Draper and Mitchell 2001;Walkem 2007), the country lags significantly behind in scholarship and policy innovations on this issue compared with the United States (Haluza-Delay 2007).In the United States, an EJ policy framework, including a unique Indigenous and tribal component, has existed now for two decades.Having said this, US policies have thus far failed to adequately address environmental injustices in many instances, as aptly demonstrated in the case of the Dakota Access Pipeline project noted by Kyle Whyte (2017) and other contributors to this volume.Criticisms and limitations of EJ efforts in the United States have been well documented by Indigenous peoples and other groups (Trainor et al. 2007).Various US tribes have asserted that their unique legal-political status affords them a set of considerations that are clearly not accommodated in the current EJ framework.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".