Unravelling cross-scale and cross-level challenges in Ethiopian forest and landscape restoration governance
Bibliographic record
Abstract
Ethiopia’s federal government has committed to one of the most ambitious forest and landscape restoration targets as part of the Bonn Challenge. To achieve the targets, actors at multiple governance levels aim to influence relevant ecological processes, drawing particular attention to the governance processes that are used to translate national restoration targets into local action. We take a multilevel governance approach and focus on the cross-scale and cross-level challenges that arise in Ethiopia’s forest and landscape restoration (FLR) governance context. To this end, we analyze public and non-state actor-led efforts related to participatory forest management and area enclosure in the Kafa Biosphere and Mount Guna landscapes. From 56 semi-structured interviews, 14 focus group discussions, and a policy and project document review, we identified five cross-scale and cross-level challenges: (1) short-term tree planting campaigns and quota mismatch with restoration timelines; (2) planning horizons of restoration-related international development projects mismatch with restoration timelines; (3) federal and international budget allocation for alternative livelihoods mismatches with sustained local restoration processes; (4) federal forest and land policies mismatch with the secure land tenure conditions needed to sustain local restoration efforts; and (5) misalignment of the forest and landscape restoration portfolio exists in the cascading government structure. The need to achieve and sustain national FLR targets requires increased focus on how existing and future restoration-related governance arrangements create fit with the temporal and spatial dimensions of forest and landscape restoration processes, and on how governance arrangements create alignment between governance levels.
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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".