Incorporating Indigenous Methodologies for Assessing Conservation Risk
Bibliographic record
Abstract
Formally designating an area as an “ecosystem at risk” can have intentional and unintentional consequences for how that area is treated, including: • The area being formally listed under pieces of legislation • Affecting the ways in which Indigenous nations interact with species or ecosystems in an area • Feeding into decision-making processes like land use planning, environmental assessments, and mitigation • Informing conservation targets, funding, or opportunities for establishing various types of protected areas But what goes into considering an ecosystem at risk? NatureServe and the International Union for Conservation of Nature (IUCN) have developed standard and respected methods for assessing the risk of ecosystems being lost. However, systems that are often used for describing and classifying ecosystems were not explicitly designed with consideration of Indigenous Knowledge. Furthermore, methods that are typically used for assessing threats to ecosystems often do not account for Indigenous cultural practices that maintain these ecosystems and their unique biodiversity over time. We will provide some examples of potential Indigenous cultural ecosystems, highlighting several that occur near the Salish Sea and elsewhere in British Columbia, Canada. Our work is at an early stage, but we will suggest that partnering with Indigenous knowledge-holders to review and improve established methods for describing, classifying, and assessing ecosystems could lead to better outcomes for conservation of biodiversity in general, and specifically ecosystems at risk.
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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 | 0.050 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".