The political ecology playbook for ecosystem restoration: Principles for effective, equitable, and transformative landscapes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The urgency of restoring ecosystems to improve human wellbeing and mitigate climate and biodiversity crises is attracting global attention. The UN Decade on Ecosystem Restoration (2021–2030) is a global call to action to support the restoration of degraded ecosystems. And yet, many forest restoration efforts, for instance, have failed to meet restoration goals; indeed, they worsened social precarities and ecological conditions. By merely focusing on symptoms of forest loss and degradation, these interventions have neglected the underlying issues of equity and justice driving forest decline. To address these root causes, thus creating socially just and sustainable solutions, we develop the Political Ecology Playbook for Ecosystem Restoration. We outline a set of ten principles for achieving long-lasting, resilient, and equitable ecosystem restoration. These principles are guided by political ecology, a framework that addresses environmental concerns from a broadly political economic perspective, attending to power, politics, and equity within specific geographic and historical contexts. Drawing on the chain of explanation , this multi-scale, cross-landscapes Playbook aims to produce healthy relationships between people and nature that are ecologically, socially, and economically just – and thus sustainable and resilient – while recognizing the political nature of such relationships. We argue that the Political Ecology Playbook should guide ecosystem restoration worldwide.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it