Supplementary material from "Disrupting and diversifying the values, voices and governance principles that shape biodiversity science and management"
Bibliographic record
Abstract
With climate, biodiversity and inequity crises squarely upon us, never has there been a more pressing time to rethink how we conceptualize, understand and manage our relationship with Earth's biodiversity. Here, we describe governance principles of 17 Indigenous Nations from the Northwest Coast of North America used to understand and steward relationships between humans and other components of nature. We then chart the colonial origins of biodiversity science and use the complex case of sea otter recovery to illuminate how ancestral governance principles can be mobilized to characterize, manage and restore biodiversity in more inclusive, integrative and equitable ways. To enhance environmental sustainability, resilience and social justice amid today's crises, we need to broaden who benefits from and participates in the sciences of biodiversity by diversifying the values and methodologies that shape such initiatives. In practice, biodiversity conservation and natural resource management need to shift from centralized, siloed approaches to those that can accommodate plurality in values, objectives, governance systems, legal traditions and ways of knowing. In doing so, developing solutions to our planetary crises becomes a shared responsibility.This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.849 | 0.406 |
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".