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Record W4414040020 · doi:10.1111/joac.70037

Cultivating Climate Precarity: Mechanisms of Surplus Capture and Immiserizing Growth in Guatemala's Horticultural Export Sector

2025· article· en· W4414040020 on OpenAlexafffund
S. Ryan Isakson

Bibliographic record

VenueJournal of Agrarian Change · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto ScarboroughSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsAgrarian societyValue (mathematics)AgricultureProsperityPovertyPoliticsAgrarian structureIndigenous

Abstract

fetched live from OpenAlex

ABSTRACT In recent decades, prominent development organizations have promoted market inclusion and agricultural value chain integration as pathways to rural prosperity in the Global South. Focusing upon the experiences of Indigenous Kaqchikel peasants in Guatemala's horticultural export sector, this paper offers a cautionary tale. Drawing upon Carmen Diana Deere's pioneering work on the political economy of agrarian change, I examine how mechanisms of surplus transfer have been reconfigured and intensified through the incorporation of peasants into export markets for fresh fruits and vegetables. Fusing Deere's framework with insights from the political ecology literature on climate change adaptation, I show how development initiatives that promise inclusion and poverty alleviation can, paradoxically, deepen socio‐economic inequality and environmental vulnerability. Guatemala has emerged as a prominent exporter of horticultural products and the sector generates substantial profits and foreign exchange. Yet the historical marginalization of Kaqchikel farmers means that they are often adversely incorporated into agricultural value chains. Their integration into exploitative market relations has produced mounting debts and deepening environmental precarity. The result is a stark example of immiserizing growth.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.288

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 studies0.0000.000
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 head0.025
GPT teacher head0.222
Teacher spread0.197 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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