Bringing Together Indigenous Knowledge and Simulation Modelling to Assess Cumulative Impacts to Indigenous Land Use in Northeastern Alberta, Canada
Bibliographic record
Abstract
The capacity of environmental impact assessment to adequately consider cumulative effects on Indigenous territories in Canada is limited by several deficiencies, including the problematic use of recent or current state as a baseline, adopting a narrow spatial and temporal scope, and marginalizing Indigenous knowledge and experiences. The result is an environmental assessment process that strains Indigenous community resources yet fails to address concerns. Using the Fort McKay Métis Nation (FMMN) of northeastern Alberta as a case study, Indigenous knowledge and simulation modeling were brought together in pursuit of a more comprehensive assessment of cumulative effects on Indigenous land use. Focus group meetings and interviews established that the ability of FMMN members to hunt, fish, trap, and harvest in their territory has rapidly diminished in recent decades, a finding also documented by a simulation that reconstructed landscape changes occurring over the past 120 years across a 103,000 km 2 region. The direct footprint from industrial activities, negligible until the 1970s, has since expanded to over 5000 km 2 , largely due to oil sands and forestry development. Although impacts to moose, fisher, and low-bush cranberry habitat have been relatively minor, opportunity for Indigenous land use has declined substantially due to the effects of industrial footprint, protected areas, and military sites on the accessibility of the land. To facilitate application of this strategic perspective to project assessment, a web application was developed that allows FMMN to assess impacts of development proposals in combination with other past and potential future development relative to a natural baseline.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".