Frontiers are Frontlines: Ethnobiological Science against Ongoing Colonialism
Bibliographic record
Abstract
Ethnobiologists are capable of making transformative scientific contributions when they participate in localized direct actions and acts of colonial dissent. Direct action tactics like blockades, protests, and re-occupations of territories are often used as (alternative) approaches for marginalized and disenfranchised communities who face expensive and oppressive justice systems. As natural resource extraction and development in settler nations continues to have uneven impacts on Indigenous Peoples and communities, this research reviews the long history of resistance to colonial expansion on the “frontier” of northwestern British Columbia, Canada. Currently, an emergent trend for legalizing and legitimizing resource extraction in rural and frontier communities is through consultation and impact assessment processes. These processes can undermine scientific rigor and hierarchies of knowledge that undercut Indigenous Peoples' knowledge, and rights to use and be on their territories. Using ethnobiological research methods to fuse cultural and natural scientific prescriptions of land use, we consider how cultural resistance camps—primarily Lelu Island, but also Madii Lii—are troves of Tsm'syen and Gitxsan experiential knowledge and cultural exchange, while resisting powerful and well-funded liquid natural gas (LNG) development in traditional territories. Ethnobiologists working in these contexts are challenged to support and stand behind their Indigenous colleagues to transform the frontier into a frontline and foster rigorous scientific research alongside Indigenous resistance.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.020 | 0.136 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".