FIELDS OF SORROWS AND HARVEST: COPING WITH GRIEF, CLIMATE CHANGE AND FOOD SCARCITY IN INDIGENOUS FARMING COMMUNITIES IN NIGERIA
Bibliographic record
Abstract
This study explores the intertwined challenges of climate change, food scarcity, and emotional grief among indigenous farming communities in Nigeria. As climate change exacerbates food production issues, Indigenous farmers face not only economic hardship but also profound psychological impacts. This research examines the coping mechanisms and resilience strategies employed by these communities. Using a combination of surveys, interviews, and focus groups, the study provides a nuanced understanding of how climate-induced food insecurity affects both the livelihoods and emotional well-being of indigenous farmers. The results reveal a complex web of challenges, emphasizing the need for targeted policy interventions and support systems. The findings points to the importance of integrating mental health support into agricultural and environmental policies. This research contributes to the broader discourse on climate justice, emphasizing the unique vulnerabilities and strengths of indigenous populations in the face of global environmental changes.
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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 | 0.007 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".