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Record W4400212902 · doi:10.1080/14650045.2024.2366316

Making Space for Feminist Decolonial Geographies of Peace with the Shuar in the Ecuadorian Amazon: A Case for ‘Cuerpo Territorio’

2024· article· en· W4400212902 on OpenAlexafffund
Martina Jakubchik‐Paloheimo, Shuar Kakaram de Buena Esperanza

Bibliographic record

VenueGeopolitics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsQueen's University
FundersInternational Development Research Centre
KeywordsAmazon rainforestSpace (punctuation)GeographyGender studiesSociologyPhilosophy

Abstract

fetched live from OpenAlex

This article follows urgent calls from peace and conflict studies and geographies of peace to be decolonised. Our study shows that for the Shuar communities in the Ecuadorian Amazon, the concept of peace is quite different from that of the Ecuadorian state. This study demonstrates how ‘Western’ centric definitions of the term are rooted in colonial logic and power structures. In this article, we explore what it could mean to decolonise theories and spaces for peace. Through encounters with Indigenous Shuar understandings of peace in a community-based participatory research project, we highlight the plurality of possibilities for the term. Using a decolonial lens we conclude with a call to action for scholars in those disciplines to engage with the Feminist Indigenous Latin American and Caribbean methodology and epistemology ‘cuerpo territorio’ (body-territory) to understand territory from the perspective of Abya Yala, that sees women’s bodies as the ‘first territory’. We argue that ‘cuerpo territorio’ is well suited to do decolonial work and help us step outside a Westernized understanding of peace and make strides towards the pluriverse. While typically this Indigenous concept has been used to better understand violence surrounding extraction sites, we propose that engaging with the methodology will prove useful for theoretical findings in spatial understandings of peace and its praxis.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.043
GPT teacher head0.352
Teacher spread0.309 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2024
Admission routes2
Has abstractyes

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