Addressing Historical Impacts Through Impact and Benefit Agreements and Health Impact Assessment: Why it Matters for Indigenous Well-Being
Bibliographic record
Abstract
The Northern Review 41 (2015): 81–109Environmental Assessment and related permitting processes have long struggled to identify and mitigate health and well-being impacts associated with resource development, especially in northern, largely Indigenous, jurisdictions. An opportunity to address this governance deficit has seemingly been provided through the growing use of mechanisms such as Impact and Benefit Agreements (IBAs) and Health Impact Assessments (HIA). Their emergence has coincided with a growth in social determinants of health research that recognizes diverse concepts and complex drivers of Indigenous well-being; it is increasingly common for researchers to speak of the ”good life” and to recognize health disparities that are based in experiences of poverty, stress, trauma, cultural erosion, and environmental dispossession. Unfortunately, little of this research has come to influence contemporary HIA practices and the content or implementation of IBAs. Missing from these novel governance mechanisms is recognition that present-day resource development is complicated by legacies of colonialism and assimilation policies, which impact Indigenous well-being. In short, what matters to Indigenous communities and what is captured in an IBA or HIA seldom coincide. This argument is supported by evidence of Indigenous participation in the Wishbone Hill HIA in Alaska and the IBA signed in support of the Meadowbank Mine in Nunavut. Given this evidence, this article calls for refinement of governance mechanisms such as IBAs and HIAs in order to better understand and respond to the complexities that inform Indigenous well-being.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".