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Record W4416830023 · doi:10.1111/aman.70044

How to Fish With Respect: A Transformation of Human‐Fish Relations in Riverside Amazonia

2025· article· en· W4416830023 on OpenAlexafffund
Vinicius de Aguiar Furuie

Bibliographic record

VenueAmerican Anthropologist · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsThe Scarborough Hospital
FundersFogarty International CenterConnaught FundPrinceton UniversityHigh Meadows Environmental Institute, Princeton UniversityWenner-Gren Foundation
KeywordsCommodificationAmazon rainforestFish <Actinopterygii>State (computer science)NormativePoliticsTransformation (genetics)

Abstract

fetched live from OpenAlex

ABSTRACT Riverside inhabitants of the Middle Xingu River Basin, in the Brazilian Amazonia, frequently say that it is important to respect animals and the forest spirits who protect them. In recent decades, however, the development of an iced fish industry in the region has changed what respect means and how it is expressed when it comes to fishing. This article analyzes shifting attitudes toward fish by focusing on the transformation of the notion of panema , a word that denotes an acquired state in which one becomes incapable of hunting and fishing. I argue that the commodification of fresh fish has not altered the animistic principle that fish are imbued with an interior state of mind and other markers of personhood. Commodification, however, has decreased the importance of spirit owners and increased the importance of the mental state of the fishers in their accounts of their activities. I pose that such transformation can only be properly accounted for by breaking down the analytical distinction between political economy and ontology and focusing on how the riverside inhabitants see it as a normative issue, measured in regard to respect.

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.

How this classification was reachedexpand

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.012
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.286
Teacher spread0.273 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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