Climate services for food security in Guatemala: An exploration of institutional dynamics in a colonial and neoliberal system
Bibliographic record
Abstract
Several governmental and nongovernmental institutions in Guatemala have been tasked with tackling the country’s problem of food insecurity. Although food insecurity has a variety of causes, the issue of climate change is beginning to attract initiatives to address the problem. Thus, Guatemalan institutions have begun utilizing climate services (CSs) to provide climate projections (of six months) for decision-making in agriculture. These services are communicated through agroclimatic bulletins that provide advice to peasants and small farmers on agricultural practices, particularly relating to beans, corn, coffee, and vegetables. While most research in this area has focused on small farmers and peasants, the present study focuses on international and Guatemalan institutions as well as the CS advocates and the governmental officials who implement these services. Through semi-structured interviews, participant observation, and a review of institutional reports, we see that the CSs tend to be implemented in a way that CSs advocates neglect the colonial and neoliberal dynamics. Drawing on the concept of climate coloniality, this article shows that despite efforts of inclusion, vulgarization, and coproduction of knowledge, the technical discussion displaces other deeper discussions, such as unequal access to land and water and institutional racism, which have been underscored by several Guatemalan academics. The promise of modernity and discourse of progress dominate the Ministry of Agriculture, both in reports and speeches and conversations with public officials.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".