Cultivating Climate Precarity: Mechanisms of Surplus Capture and Immiserizing Growth in Guatemala's Horticultural Export Sector
Bibliographic record
Abstract
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".