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Record W4308566832 · doi:10.1111/joac.12523

Paying for ecological services in Ecuador: The political economy of structural inequality

2022· article· en· W4308566832 on OpenAlexafffund
Matthew McBurney, Luis Alberto Tuaza Castro, Craig Johnson

Bibliographic record

VenueJournal of Agrarian Change · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousLivelihoodAgrarian societyCarbon sequestrationPoliticsContext (archaeology)State (computer science)Payment for ecosystem servicesPolitical scienceEcosystem servicesGeographyNatural resource economicsEconomicsEcologyAgriculture

Abstract

fetched live from OpenAlex

Abstract Paying Indigenous communities to conserve land for carbon sequestration is a controversial way of tackling climate change. Critics argue that paying for ecological services (or ‘PES’) in the form of carbon offset programmes reduces land and social relations to an economic transaction that devalues Indigenous livelihoods and communities. At the same time, empirical studies have shown that Indigenous communities have accepted and even embraced the idea of being paid to conserve land for climate change mitigation. This paper explores this apparent contradiction by investigating the implementation of Programa Socio Bosque (PSB), a PES carbon sequestration programme in Ecuador. Drawing upon primary fieldwork in the highland province of Chimborazo, it makes the case that PES programmes need to be understood as form of state power that reconfigures and reinforces the ways in which Indigenous peoples engage with the state. Particularly important in this regard is the role of the state in reinforcing the agrarian conditions under which Indigenous communities use and interpret PES payments while at the same time reconfiguring new forms of land conservation. Empirically, the research reveals important complementarities between the goals of carbon sequestration PES programmes and Indigenous land‐use practices. Methodologically, it highlights the importance of situating the study of PES programmes in a context of land struggles, community–state relations and agrarian change.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.619

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.248
Teacher spread0.203 · 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 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

Citations6
Published2022
Admission routes2
Has abstractyes

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