Making Space for Feminist Decolonial Geographies of Peace with the Shuar in the Ecuadorian Amazon: A Case for ‘Cuerpo Territorio’
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".