Tipping Points in First Nations' Land Use: Understanding the Impacts of Industrial Development in Northeast British Columbia
Bibliographic record
Abstract
This study examines the impact of industrial development on the land use practices of the Saulteau First Nations (SFN) in northeastern British Columbia. Using household surveys and spatial data analysis, the goal is to identify critical tipping points at which industrial activities disrupt SFN members’ traditional harvesting practices.\nThe 12-month household survey revealed that most of the SFN harvest occurred near the SFN reserve, although some distant locations in the north were used to target specific species. Additionally, the northwest region of the SFN reserve offered better hunting opportunities compared to the east region, which faced extensive oil and gas developments.\nSignificant constraints to SFN land use were identified as human-induced disturbances, such as industrial development, environmental change, food contamination, and recreational development. Spatial analysis revealed areas of conflict between industrial activities and SFN harvesting practices, particularly near the SFN reserve and municipality centre.\nThis study proposes an industrial tipping point model and identifies a cessation tipping point (industry density = 0.084 km2/km2) where industrial development significantly hinders SFN's traditional land use practices. The results also highlight the subjective and varied nature of determining a tipping point influenced by specific land use activities and household assets.\nThe research findings are intended to facilitate constructive dialogue between the SFN community and the provincial government of British Columbia to preserve environmental livelihoods and territorial rights. The study emphasizes the importance of conservation and restoration efforts in core areas of the SFN territory. The insights provided can assist the Regional Strategic Environmental Assessment Project Team in striking a balance between sustainable economic development and the rights of First Nations in the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".