Environmental and socio-cultural impacts of glyphosate-based herbicides: perspectives from indigenous knowledge and western science
Bibliographic record
Abstract
Introduction For decades, herbicide application in commercial forestry has been a serious concern for First Nations across northern Ontario. To date, the vast majority of studies concerning the impacts of glyphosate-based herbicides have been conducted through a Western scientific lens. Indigenous knowledge systems provide holistic frameworks which acknowledge the interconnectedness of the environment and provide a holistic view of relationships between flora, fauna, environment, and humans. This socio-ecological perspective is key to understanding the wide-ranging impacts of commercial forestry on the wellbeing of Indigenous Peoples. Methods Through a knowledge sharing workshop, “Connecting Guardians in a Changing World”, we interviewed community members from First Nations across the Robinson-Huron Treaty Area and asked them to share their knowledge and concerns regarding herbicide use in commercial forestry. Based on the topics discussed in the workshop, we conducted a literature review to further investigate documented Western scientific evidence on workshop participants’ concerns. We then wove the responses of participants and Western Science to identify key concerns of participants regarding the use of glyphosate-based herbicides, identify gaps in current knowledge, and to direct future research. Results We identified three main research gaps regarding glyphosate-based herbicide impacts used in forestry: 1) research regarding the direct effects of glyphosate-based herbicide toxicity to most of the fauna and flora of the Great Lakes region of northeastern Ontario as well as its environmental persistence, 2) research regarding the indirect effects of glyphosate-based herbicides to the ecosystem and resulting impacts of trophic cascades, and 3) research regarding the impacts of glyphosate-based herbicides to Indigenous culture and community wellbeing. Discussion By weaving knowledge systems in this way, we can conduct research through an inclusive system which prioritizes the inclusion of multiple ways of knowing, addressing environmental concerns in holistic and inclusive ways which emphasize the interconnectedness of the environment, including humans therein.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".