“Where the Moose Were”: Fort William First Nation’s Ancestral Land, Two–Eyed Seeing, and Industrial Impacts
Bibliographic record
Abstract
A two-eyed seeing approach considered Indigenous knowledge and Western science towards eco–health, reconciliation and land back with Fort William First Nation (FWFN) in Ontario, Canada. To map traditional land use, occupancy, and ecological knowledge, we interviewed 49 FWFN members about their hunting, fishing, trapping, plant harvesting, cultural sites, and sacred gatherings on their ancestral land. Their traditional land use and occupancy includes more than 7.5 million ha of their ancestral land. The FWFN members reported many industrial impacts on their reserve and ancestral land. We analyzed the normalized difference vegetation index (NDVI) change over time on FWFN’s ancestral land and the Thunder Bay Pulp and Paper Mill (TBPP)’s National Pollutant Release Inventory data to investigate the FWFN members’ ecohealth concerns. The NDVI analysis revealed large tracts of degraded FWFN’s ancestral land due to logging areas, mining claims, settlements, and paper mills. Mining claims and greenstone belts occupy a quarter of the FWFN members’ ancestral land. The TBPP mill dumped pollution into the Kaministiquia River upstream and upwind of the FWFN community, exposing FWFN members to kilotons of cancerous and other toxic chemicals each year for over a century. Resource extraction and pollution in Northwestern Ontario negatively impacted the human health and ecosystem integrity of FWFN, requiring reconciliation by restoring damaged land and preventing pollution as the starting point for land back. The first step to land back is ending the environmental racism of the TBPP’s pollution directed downstream and downwind of FWFN and protecting ancestral land against logging, mining, and other extractive industries.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".