Indigenous‐led research on traditional territories highlights the impacts of forestry harvest practices on culturally important plants
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Indigenous knowledge and governance are critical to successful conservation and Indigenous Peoples' ability to live sustainably on their lands. However, various industrial land use practices impact the conservation value and traditional resources these lands provide. Here, we evaluated the effects of harvest, glyphosate application, and fire on 51 edible and medicinal plant species identified by traditional knowledge of Indigenous Peoples in the western boreal forest of Canada, a landscape of rapid industrialized landscape change. We collected vegetation data between 2007 and 2020 and used linear models and machine learning to model the richness and abundance of edible and medicinal plant species. Glyphosate application and harvest best explained the richness and abundance of species. Despite our models' indication that species richness and abundance were higher in harvested and treated study sites, detailed qualitative data based on local Indigenous knowledge suggest these forestry practices negatively impacted Indigenous Peoples' ability to use traditional plants. Importantly, plants in areas treated with glyphosate were unsuitable for human consumption and exhibited abnormal color and flavor presentations. Concerns over access to traditional resources are increasingly important as industrial impacts continue to expand globally. Thus, we hope that this Indigenous‐led study design leveraging both quantitative and qualitative data can result in successful partnerships that better reflect the environmental concerns of Indigenous Peoples.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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