The political ecology playbook for ecosystem restoration: Principles for effective, equitable, and transformative landscapes
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".