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Record W4413098848 · doi:10.1080/17477891.2025.2538510

The everyday of inundation: livelihoods and lifeways dimensions of flooding experience in Amazonian Peru

2025· article· en· W4413098848 on OpenAlex
Jennifer C. Langill, Christian Abizaid

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Hazards · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoConference of Latin Americanist Geographers
KeywordsAmazonianLivelihoodFlooding (psychology)GeographyEnvironmental planningEnvironmental resource managementAmazon rainforestEnvironmental protectionArchaeologyEnvironmental scienceEcologyAgriculturePsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.244
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it