Addressing Historical Impacts Through Impact and Benefit Agreements and Health Impact Assessment: Why it Matters for Indigenous Well-Being
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it