Bibliographic record
Abstract
Indigenous Knowledge Systems arise from place-based relationships with the world in concert with healthy ecosystems, which encompass Non-Human People. Assuming conservation science fundamentally reaches for the goal of healthy, functioning ecosystems, conservation from non-Indigenous and Indigenous perspectives may share similar purposes. However, if dominant western cultures engage with Indigenous ones toward shared conservation goals without critical use of decolonial thinking and challenging some of the basic tenants of western culture, violence against Indigenous Knowledge Systems may ensue. Drawing mainly from lessons in a Canadian context, I highlight several points to help avoid violence against Indigenous Knowledge Systems. These points challenge western scientists to think on the nature of knowledge, consider their own worldview and those of Indigenous Peoples, explore concepts of Ethical Space, consider science as a tool, challenge basic terminology, consider scales in thinking, delve into thought structures as tools, and center Indigenous Knowledges. We must all push towards the flourishing of all knowledge systems, including those of both human and Non-Human People, to face biodiversity and climate crisis and to shift some fundamental assumptions of dominant Western Knowledge Systems.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.157 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| 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".