Water up to our necks: learning and responses to hydroclimatic variability in Brazilian Amazon floodplain communities
Bibliographic record
Abstract
Understanding how floodplain communities of the Brazilian Amazon respond to the impacts of extreme flooding induced by hydroclimatic variability and how learning supports these responses are the dual focus of this thesis. The UN Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (2014) demonstrates that rural communities in developing countries are among those most impacted by extreme climatic events, which are likely to increase in frequency and intensity in the near future. However, the community-based adaptations (CBA) literature indicates that rural communities have coped with climate variability by using a range of local assets, especially when governments have failed to provide proper assistance. My study followed a qualitative approach, employing semi-structured interviews with community members and institutional agents, participant observation, participatory mapping exercises, and validation workshops. Findings demonstrate that the repeated occurrence of extreme floods between 2009 and 2015 resulted in severe impacts, including some that had never been experienced by the local communities, such as the complete loss of perennials. Utilizing the sustainable livelihoods and resilience lenses, I investigated the locally-devised short-term and long-term responses to these impacts. Results revealed a wide range of responses, some of which I placed in a newly-proposed category of annual responses. Data about the capacity to absorb impacts without responding and about transformative responses were also provided. I also found that much of the learning that was foundational to the responses was instrumental in nature. The learning outcomes for individual participants resulted in proposing two new learning domains –introspective and emancipatory learning. Transformative outcomes were revealed for some participants who found that the intensity and repetition of extreme flooding drove them to leave the floodplain for upland or urban areas. Findings also revealed a wide array of learning domains and sources of individual learning, such as experience, dialogue, reflection, and observation, that contributed to expanding the applicability of the transformative learning theory. Lessons drawn from community experiences on how to live with hydroclimatic changes demonstrate that continuous learning through multiple sources is essential for helping local people increase their capacity to overcome uncertainties. Learning is also fundamental for communities to build a wider range of possible responses to be chosen from and applied with agility in order to decrease vulnerability to increasingly variable, dynamic and unpredictable impacts.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".