Governance principles for community‐centered conservation in the post‐2020 global biodiversity framework
Bibliographic record
Abstract
Abstract Strategies to protect biodiversity in the face of a global crisis must be place‐based and sensitive to context. A failure to consider the socioeconomic and political circumstances, as well as wellbeing needs and lived realities of those most directly reliant upon biodiversity will further undermine progress on Aichi targets and subsequent goals for the post‐2020 framework. How communities experience the benefits or costs of conservation action is influenced in large measure by the principles that guide conservation governance, and the subsequent institutional structures and processes that frame conservation action (at local to global scales). In this article, we define and critically reflect on core principles of community‐centered conservation governance needed to yield desirable and long‐term conservation outcomes—both ecological and social (i.e., equitable and just). In doing so, we emphasize a conception of community‐centered conservation that we argue is relevant to guide implementation of a post‐2020 biodiversity framework, and which is based on a foundation of well‐established evidence. Core principles of community‐centered conservation governance include: (a) building multilevel networks and collaborative relationships needed to coproduce conservation solutions; (b) promoting equity and recognizing the central role of women as agents of positive change in conservation efforts across scales; (c) reframing conservation action through the lens of reconciliation and redress (e.g., responding to injustices from land grabs and territorial enclosures); (d) ensuring a rights‐based approach to conservation action in which community agency, access and decision making autonomy are supported; and (e) revitalizing the customary and local institutions that provide legitimate and adaptive strategies for the stewardship of biodiversity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 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".