Different perspectives of deforestation in the Peruvian Amazon : An interview study based in Cusco and Pilcopata in the Cusco region
Bibliographic record
Abstract
The Amazon rainforest constitutes a quarter of the global biodiversity and is responsible for 15% of the terrestrial photosynthesis. It is important to protect the forest and limit the deforestation to not release more greenhouse gases, which aggravate climate change. There is a big problem today with deforestation, especially in the Peruvian Amazon. In Peru is the main driver to deforestation human activity, such as road infrastructure, markets and agriculture. It is most common with small-scale agriculture, especially in the Cusco region where this study is conducted. How the deforestation should be managed or if current instruments are working is there different perspectives on. In this essay are the different perspectives regarding the exploitation of the Amazon and its impacts being analyzed. The essay has an interview method and the interviews are carried out on members of non-governmental organizations and Cusco regional government. Our analysis shows that the different perspectives show similarities regarding the main drivers for the deforestation, the information and knowledge-gap regarding the impacts of deforestation. There is, however, a difference in the perspectives regarding funds. One perspective from the non-governmental members is that there is not enough funding to receive as well as that the funds that do exist go to people and projects that do not do the work properly.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".