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Record W4413957566 · doi:10.1177/0094582x251367784

Participation Artifacts: Conservation and Climate Governance with Indigenous Amazonian Communities

2025· article· en· W4413957566 on OpenAlexfundno aff
Maritza Paredes, Anke Kaulard, Danitza Gil

Bibliographic record

VenueLatin American Perspectives · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsAmazonianIndigenousCorporate governanceEnvironmental governancePolitical scienceGeographyClimate governanceEnvironmental planningClimate changeEnvironmental resource managementAmazon rainforestBusinessEcologyEconomicsBiology

Abstract

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Indigenous participation is increasingly recognized as critical for effective climate change governance. However, a gap persists between global commitments and their implementation at local levels. This paper examines the challenges of participation in forest conservation initiatives, particularly how “participation artifacts”—tools, methodologies, and mechanisms designed to facilitate participatory processes—shape Indigenous inclusion in climate governance. Drawing on Bruno Latour’s actor-network theory, we investigate how these artifacts mediate power dynamics and influence decision-making processes. Through a mixed-methods approach including interviews, participatory observation, and document analysis, we find that participation artifacts often create the appearance of inclusion while reproducing the marginalization of Indigenous communities. Despite the presence of advanced participatory mechanisms, these processes fail to address Indigenous priorities and perspectives effectively, perpetuating political and social exclusion. Instead of contributing to climate justice, such mechanisms maintain power imbalances, undermine Indigenous autonomy, and jeopardize access to forests, which are essential for Indigenous livelihoods. Findings highlight the urgent need to move beyond mere technical compliance with participatory norms toward a more genuine engagement with Indigenous knowledge, leadership, and priorities. La participación indígena es cada vez más reconocida como un componente fundamental para una gobernanza climática efectiva. No obstante, persiste una brecha entre los compromisos asumidos a nivel global y su implementación en los ámbitos locales. Este artículo analiza los desafíos que plantea la participación en las iniciativas de conservación forestal, con especial énfasis en el modo en que los “artefactos de participación” —herramientas, metodologías y mecanismos diseñados para facilitar procesos participativos— configuran la inclusión indígena en la gobernanza del clima. A partir de la teoría del actor-red de Bruno Latour, se examina cómo dichos artefactos median las dinámicas de poder e inciden en los procesos de toma de decisiones. Mediante un enfoque metodológico mixto que incluye entrevistas, observación participante y análisis documental, se observa que los artefactos de participación suelen generar una apariencia de inclusión que, en realidad, reproduce la marginación de las comunidades indígenas. A pesar de la existencia de avanzados mecanismos participativos, estos procesos no logran incorporar de manera efectiva las prioridades ni las perspectivas indígenas, perpetuando así su exclusión política y social. En lugar de promover la justicia climática, dichos mecanismos tienden a preservar los desequilibrios de poder, socavar la autonomía indígena y poner en riesgo el acceso a los bosques, fundamentales para sus medios de vida. Los hallazgos evidencian la necesidad urgente de trascender el cumplimiento meramente técnico de las normas de participación y avanzar hacia un involucramiento genuino con los conocimientos, el liderazgo y las prioridades de los pueblos indígenas.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.010
Scholarly communication0.0060.004
Open science0.0010.009
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2025
Admission routes1
Has abstractyes

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