Progress and challenges in consolidating the management of Amazonian protected areas and indigenous territories
Bibliographic record
Abstract
Abstract Effective management refers to the ability of a protected area or indigenous territory to meet its objectives, particularly as they relate to the protection of biodiversity and forest cover. Effective management is achieved through a process of consolidation, which among other things requires legally protecting sites, integrating sites into land‐use planning, developing and implementing management and resource‐use plans, and securing long‐term funding to pay for recurrent costs. Effectively managing all protected areas and indigenous territories in the Amazon may be needed to avoid a deforestation tipping point beyond which regional climatic feedbacks and global climate change interact to catalyze irreversible drying and savannization of large areas. At present, protected areas and indigenous territories cover 45.5% (3.55 million km 2 ) of the Amazon, most of the 60–70% forest cover required to maintain hydrologic and climatic function. Three independent evaluations of a long‐term large‐scale philanthropic initiative in the Amazon yielded insights into the challenges and advances toward achieving effective management of protected areas and indigenous territories. Over the life of the initiative, management of sites has improved considerably, particularly with respect to management planning and capacity building, but few sites are effectively managed and many lack sufficient long‐term financing, adequate governance, support of nongovernmental organizations, and the means to withstand economic pressures. The time and money required to complete consolidation is still poorly understood, but it is clear that philanthropic funding is critical so long as essential funding needs are not met by governments and other sources, which could be on the order of decades. Despite challenges, it is encouraging that legal protection has expanded greatly and management of sites is improving steadily. Management of protected areas in other developing countries could be informed by improvements that have occurred in Amazonian countries.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".