Building alliances for territorial management in forest-based landscapes: the case of Caçador Model Forest in southern Brazil
Bibliographic record
Abstract
Model Forests (MFs) are social and participatory processes aiming at the sustainable development of a territory where the forest plays an important role. Individuals and organizations share knowledge and combine expertise and resources to provide income-generating opportunities, balancing social, economic, and ecological values. The concept originated in the late 80s, in Canada, and was launched internationally in Rio-92 Conference under the name of 'Model Forests', which adopts network strategies ('International Model Forest Network - IMFN' and regional networks as the Latin America Caribbean Model Forests Network (LAC-Net)). In Brazil, the system is coordinated by the Brazilian Forest Service and to date consists of two MFs in the Southeast and one in the South. The latter is located in the region of Araucaria Forest, one of the most endangered ecosystems of the Atlantic Forest. The high degree of landscape fragmentation and a very restrictive environmental legislation regarding the use of native forest are both major drivers of a well-known polarization between ?development? and ?conservation? viewpoints. Those conflicts of interests combined with low Human Development Indices, poor income distribution and environmental liabilities observed in the municipality of CaA§ador, in Santa Catarina State, motivated the creation of a MF in the region. The process is being conducted by the Brazilian Agricultural Research Corporation (Embrapa) since 2007. During the four subsequent years, public meetings and workshops were held in order to present and discuss the Model Forest approach with the local community. In 2012, individuals, organizations and local stakeholders joined the CaA§ador Model Forest Council. At the same year, the Council submitted a formal proposal for the creation of the MF and in 2013 the Model Forest area was visited by representatives of LAC-Net and IMFN. Finally, on June 17, 2013, CaA§ador Model Forest (BMCDR) was officially approved as a member of the Network, covering the entire territory of the municipality with 98,000 hectares. BMCDR mission is to provide better quality of life and environmental conservation through participatory management of the territory, strengthening family farming and the cultural identity and promoting the improvement, conservation and use of forest and water resources. The year 2014 was devoted to the process of discussing CaA§ador Model Forest governance model approaches and constructing its strategic plan, which comprises four major issues to be addressed during the next five years: promoting the local identity; use and conservation of the Araucaria Forest; use and conservation of water and the promotion and dissemination of BMCDR resources.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".