Elementos y herramientas de decisión para la conservación y manejo de paisajes altamente transformados. El caso del Bosque Modelo Risaralda (Risaralda, Colombia)
Bibliographic record
Abstract
ENG- The Model Forests are a platform for action for sustainable landscapes, which was born in Canada and after the Rio de Janeiro Meeting expanded throughout the world. This platform, in Colombia, has existed in a single territory, Risaralda Department, in Colombia, for a period of 16 years and more recently in the Aburra Valley, since 2022. The Model Forests are integrated into a Latin American Network of Model Forests. This is the first time that quantitative and qualitative methods have been applied in the RLABM to address the study of tools and methodologies focused on governance and conservation. The doctoral thesis focused on developing elements and tools for the governance and conservation of the Risaralda Model Forest. The Model Forest is a large territory located between the Central and Western Cordilleras of Colombia and whose borders coincide with the political- administrative delimitation of the Department of Risaralda. The work was approached, with qualitative and quantitative methodologies, in four components that allowed understanding landscape dynamics, understanding the transformation of these landscapes and social dynamics in these landscapes: an analysis of metrics and landscape dynamics, an analysis of networks and interrelations between actors or interested parties and a governance model design based on five criteria and seven indicators that allowed organizing extensive bigrams, performing natural language processing and subsequently using the LDA (Latent Dirichlet Allocation) probabilistic model that directed much of the work of building interrelations and contributed the elements to the governance model. The components analyzed produced some expected findings, as in the case of landscapes, where the conceptual framework of conservation biology was available, and where the most recent analysis of landscape dynamics was produced, finding significant fragmentation and the lack of cores from which the sustainability of the landscape can be guaranteed. In the case of networks, documentary sources of public policy were reviewed and interviews were conducted throughout the territory and with multiple actors or interested parties. It was found that governance, deduced from the documentary, is biased towards the institutional, although in the analyses, based on the interviews, it is the network of relationships between citizens, institutions and organizations, which appears strongly linked to decisions and governance, although not sufficiently recognized in public policy. The purpose, if one can say, was to provide the Risaralda Model Forest with a governance model that recognized what was found from the public and social aspects and that would allow strengthening efforts and decisions aimed at strengthening sustainable landscapes in the case study of the Risaralda Model Forest. It is hoped that this study will offer elements that can be replicated to other Model Forests to review their governance in transformed landscapes
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".