Resilience and tipping points in social-ecological systems of the southwestern Amazon: a participatory systems analysis
Bibliographic record
Abstract
Recent reports warn of the imminent surpassing of a tipping point that will transform the Amazon rainforest into a savannah. In the transboundary region shared by Bolivia, Brazil, and Peru (named the MAP region, an acronym for the states of Madre de Dios, Peru; Acre, Brazil; and Pando, Bolivia), this land-use conversion has been triggered by logging and farming, and recently intensified by the construction of a transcontinental highway. As economic incentives grow, so does social and environmental harm. We devised a participatory systems approach to evaluate and characterize the social-ecological system in a site in each of the three countries, and to identify the factors that may lead to the systems’ loss of resilience and eventual trespassing of tipping points. Methodologically, the approach combines stakeholder analysis, network analysis, and systems analysis, and includes transboundary research and locals’ participation as overarching premises adding plausibility and legitimacy to the findings. Results show that the three country-sites, despite their similarities, have evolved divergently into archetypal social-ecological systems, each confronting specific challenges, i.e., in Bolivia, dependence on the volatile commodity Brazil nut, and unstable politics, has weakened a previously stable and functional institutional landscape. In Brazil, the conversion of forests into pastures for cattle ranching remains the major peril, fueled by a steady market demand and, regardless of the party in power, insufficient restraining measures. In Peru, economic diversification has increased the system’s resilience as a whole, however authorities struggle to exert control over the economic activities, some of which are illegal.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".