Ten people‐centered rules for socially sustainable ecosystem restoration
Bibliographic record
Abstract
As the UN Decade on Ecosystem Restoration begins, there remains insufficient emphasis on the human and social dimensions of restoration. The potential that restoration holds for achieving both ecological and social goals can only be met through a shift toward people‐centered restoration strategies. Toward this end, this paper synthesizes critical insights from a special issue on “Restoration for whom, by whom” to propose actionable ways to center humans and social dimensions in ecosystem restoration, with the aim of generating fair and sustainable initiatives. These rules respond to a relative silence on socio‐political issues in di Sacco et al.'s “Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits” on socio‐political issues and offer complementary guidance to their piece. Arranged roughly in order from pre‐intervention, design/initiation, implementation, through the monitoring, evaluation and learning phases, the 10 people‐centered rules are: (1) Recognize diversity and interrelations among stakeholders and rightsholders'; (2) Actively engage communities as agents of change; (3) Address socio‐historical contexts; (4) Unpack and strengthen resource tenure for marginalized groups; (5) Advance equity across its multiple dimensions and scales; (6) Generate multiple benefits; (7) Promote an equitable distribution of costs, risks, and benefits; (8) Draw on different types of evidence and knowledge; (9) Question dominant discourses; and (10) Practice inclusive and holistic monitoring, evaluation, and learning. We contend that restoration initiatives are only tenable when the issues raised in these rules are respectfully addressed.
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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 | 0.118 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.126 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".