Methodology for the sustainable management of ecosystem resources towards livelihoods in the Andean area, through ecosystem-based adaptation
Bibliographic record
Abstract
Development approaches and models aimed at ecosystem-based adaptation (EbA), improve the management of resources in rural territories with a perception of worldview and ancestral knowledge of the different age-generational groups. This allows building relationships of trust and reciprocity in the socio-environmental setting, translated into scientific knowledge in action plans with a focus on mitigation and adaptation to climate change. As a proposal for this trend, the EbA-PACHA methodology arises, which analyzes vulnerability to risks changes in the life strategies of rural communities, through a process that uses an alternative solution with the EbA approach. This methodology was implemented in rural communities in the province of Chimborazo in central Andean Ecuador. A graph of the relationships with development actors in their territories was generated, establishing that the institutions that have a hierarchy in decision-making are NGOs, while government actors related to the environment have a negative pressure against the generation of alternatives and lack to contribute to a sustainable proposal. With the application of this tool, it is observed that it is necessary to influence development by identifying their life strategies, since there is a strong connection to the use of natural resources and the interaction with human resources meaning people with knowledge. Nonetheless, without the nexus of interscientific knowledge people are increasingly disconnected from their ancestral wisdom. These results confirm the worldwide indigenous world view that "all is one" as affirmed by the "Nuu-Chah-Nulth" philosophy of Canadian indigenous populations, as also increasingly seen by scientists, citizens, as the only way to a sustainable future.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".