The everyday of inundation: livelihoods and lifeways dimensions of flooding experience in Amazonian Peru
Bibliographic record
Abstract
It is widely recognised that social differences are (re)produced through environmental hazards, yet feminist foundations remain relatively absent from critical hazards scholarship. In this paper, we seek to deepen understandings of the experience of environmental hazards through a feminist lens of the ‘everyday’. We focus on the Amazon floodplains, where annual flooding is integral to rural livelihoods but where extreme floods can have devastating impacts. Using the 2014 flood year as analog, we analyze four facets of flood experience: (1) preparations, (2) impacts, (3) responses, and (4) social assistance. We identify livelihood- and lifeway-oriented dimensions of experience with a ‘bad’ flood and demonstrate how the two dimensions are deeply interrelated. We find that while livelihood-based impacts have longer-term ramifications (such as lost crops or lost trees), impacts associated with everyday living and survival (namely, inundated houses and illnesses) stand out to respondents as more consequential. We further identify forms of assistance embedded in village social norms and document how the lack of appropriate state assistance during the flood is viewed locally as perpetuating their marginalisation. In sum, we argue that the flood season, whether ‘normal’ or ‘extreme’ is an experience of people's ‘mundane everyday’, socially-embedded world, and intimate human-environment connections.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".