Deforestation, Forest Management and Governance
Bibliographic record
Abstract
Abstract The world's forests are a major source of material and fuel, a vast reservoir of biodiversity and they also provide valuable ecological services such as hydrological cycling and carbon sequestration. They play an important role in carbon accounting schemes under the Kyoto Protocol and the UN Programme on Reducing Emissions from Deforestation and Forest Degradation (REDD). As such, the impacts of global deforestation are of critical social, ecological and political significance. The primary causes of deforestation are well understood and will likely be exacerbated as new demands for forest products develop. Innovative governance arrangements, such as third party certification schemes and co‐management, have increasingly sought a role in alleviating deforestation alongside governmental forest policy‐making. Continued decreases in deforestation rates will depend on changes in how forests are valued and managed through multi‐level governance arrangements. Key Concepts: Energy production account for more than 50% of all wood uses. There is concern that woodfuel use is one of the drivers of deforestation, but it is often difficult with available statistics to understand just how important this driver is. Deforestation is mainly caused by economic forces, governance failures and politico‐ethical failures. Given the rapid global loss of biodiversity, mainly due to deforestation in tropical areas, primary and selectively logged forests are of critical importance in tropical forest conservation. Forest carbon sequestration is explicitly allowed in the Kyoto Protocol as a tool to meet emission reduction targets. Without stricter governance oversight, carbon emissions mitigation initiatives such as the UN Programme on REDD can enable the usurpation of land and resource rights from indigenous communities in developing countries. Decreasing the rate of deforestation requires the participation not just of governments but of local communities, indigenous peoples and citizens for decision‐making and monitoring in forest policy and management.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".