Bridging knowledge systems in the Amazon through co-creation for resilient water management
Bibliographic record
Abstract
The Amazon rainforest stands at the forefront of socio-ecohydrological challenges, with ever-growing extreme events such as droughts and floods disrupting ecosystems and local communities. Addressing these issues requires co-creative and transdisciplinary approaches that blend scientific knowledge with the lived experiences and expertise of diverse stakeholders. Here, we present three distinct co-creation initiatives in the Amazon, each at a different stage of development, to illustrate the transformative potential, complexities and opportunities of participatory water resources management. First, the PAB-Brasil 2024 (Brazilian Action Plan for Combating Desertification and Mitigating Drought) demonstrates the importance of multi-level co-creation in policy-making. This initiative employed a decentralised and inclusive participatory methodology, with regional seminars designed as spaces for dialogue and collaborative knowledge production. Drawing from popular education principles inspired by Paulo Freire’s critical pedagogy, the seminars in this project integrated traditional knowledge from Indigenous, Quilombola, i.e. descendants of Africans who resisted enslavement and established autonomous communities, and rural communities with scientific expertise. The process involved structured group dynamics, thematic discussions, and collective drafting of policy recommendations aimed at addressing land degradation and safeguarding water resources. The outcomes contribute to a national strategy that reflects regional needs and aligns with global frameworks such as the UN Convention to Combat Desertification. Secondly, The 3R Project, now in its implementation phase, addresses land-use pressures within the Chico Mendes Extractive Reserve in the state of Acre, Brazil, where deforestation and unregulated cattle ranching compromise water access. The methodological approach combines stakeholder interviews, spatial mapping, and policy analysis to understand the socio-political drivers of water scarcity. The project’s participatory framework prioritises local stakeholder voices, proposing the use of actor-centred workshops to collaboratively design land management solutions that mitigate water scarcity while fostering sustainable livelihoods. The initiative also builds on long-standing community relationships, ensuring that legal, social, and cultural perspectives inform the strategies. Finally, the T-SECA Project (Transdisciplinary Social Ecohydrology for Community Adaptation), in its design development phase, exemplifies a community-led research approach. Centred in the Mundurukú Indigenous territory in Pará, this initiative aims to use participatory visual social science methods such as photovoice and videovoice to capture local narratives of changes in water dynamics in the environment. In this project, community members will co-direct research priorities by documenting their lived experiences of floods and droughts through visual media. The team integrates these insights with scientific ecohydrological data, such as precipitation, streamflow, and groundwater levels, supplemented by isotope tracing to understand water sources and flows. The goal is to co-develop adaptation plans tailored to the community's needs, with outputs intended to support large-scale implementation. These three initiatives reaffirm the need for iterative, inclusive, and place-based co-creation processes in hydrology and water management. By prioritising mutual learning and power-sharing among scientists, policymakers, and local stakeholders, these initiatives aim to promote actionable solutions that are both scientifically robust and socially grounded. This presentation invites discussion on how co-creation can be scaled and diversified in hydrological sciences to address complex water challenges across diverse socio-ecological contexts.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".