Intergenerational Food Insecurity, Underlying Factors, and Opportunities for Intervention in Momostenango, Guatemala
Bibliographic record
Abstract
Achieving sustainable food security in Guatemala, where nearly half the population is food insecure and 50% of children face chronic malnutrition, is challenging. This mixed-methods study aimed to identify the impacts of climate change on food production, community food security, and household food security. Twelve agricultural group leaders in six communities were interviewed using semi-structured guides. Key informant interview themes included subsistence agriculture, commercial production, challenges related to climate, capital, market, and capacity, as well as sustainable opportunities. Fifty-five mothers from 13 distinct communities around Momostenango were surveyed and interviewed. A significant finding is that 85% of households were food insecure, with 93% relying on agriculture. Food-secure families mostly worked on their own or leased land, whereas food-insecure ones combined farming with day labor. In times of food scarcity, strategies such as altering food consumption and reducing expenses were common. Severely food-insecure families were significantly more likely to reduce portion sizes (72%), whereas food-secure families typically resorted to less preferred foods. Overall, food insecurity was notably linked to larger families, older mothers with limited education, and reliance on agricultural day labor. Food insecurity is a long-term issue in rural areas, deeply rooted in structural socioeconomic constraints, and recurring across generations.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".