Situating the “human” in forest landscape restoration
Bibliographic record
Abstract
Globally, forest landscape restoration (FLR) is gaining ground, alongside other forms of restoration under the UN Decade on Ecosystem Restoration. In most cases, projects and initiatives fail to consider human dimensions that influence the processes and outcomes of the restoration effort. These dimensions refer to how and why humans value natural resources; how humans want resources to be managed; and how humans affect or are affected by natural resource management decisions. Using the model of the forest transition curve that shows the trajectory from loss of forests to restored forests, we discuss how FLR intersects in different ways with this transition curve. We conclude that: 1) definitions and their implications are a fundamental challenge for FLR; 2) there is an intrinsic interdependence between people and forests that varies across spatial and temporal scales and that is mediated by institutions; 3) power differentials among stakeholders create imbalances in restoration; 4) conflicts around restoration result from differing interests, power and values. Equitable and durable restoration requires a much greater inclusion of human dimensions along all steps of the process.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.074 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".