Putting the pieces together: Integration for forest landscape restoration implementation
Bibliographic record
Abstract
Abstract The concept of forest landscape restoration (FLR) is being widely adopted around the globe by governmental, non‐governmental agencies, and the private sector, all of whom see FLR as an approach that contributes to multiple global sustainability goals. Originally, FLR was designed with a clearly integrative dimension across sectors, stakeholders, space and time, and in particular across the natural and social sciences. Yet, in practice, this integration remains a challenge in many FLR efforts. Reflecting this lack of integration are the continued narrow sectoral and disciplinary approaches taken by forest restoration projects, often leading to marginalisation of the most vulnerable populations, including through land dispossessions. This article aims to assess what lessons can be learned from other associated fields of practice for FLR implementation. To do this, 35 scientists came together to review the key literature on these concepts to suggest relevant lessons and guidance for FLR. We explored the following large‐scale land use frameworks or approaches: land sparing/land sharing, the landscape approach, agroecology, and socio‐ecological systems. Also, to explore enabling conditions to promote integrated decision making, we reviewed the literature on understanding stakeholders and their motivations, tenure and property rights, polycentric governance, and integration of traditional and Western knowledge. We propose lessons and guidance for practitioners and policymakers on ways to improve integration in FLR planning and implementation. Our findings highlight the need for a change in decision‐making processes for FLR, better understanding of stakeholder motivations and objectives for FLR, and balancing planning with flexibility to enhance social–ecological resilience.
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 | 0.085 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".