Integrating climate in Ugandan health and subsistence food systems: where diverse knowledges meet
Bibliographic record
Abstract
BACKGROUND: The effects of food insecurity linked to climate change will be exacerbated in subsistence communities that are dependent upon food systems for their livelihoods and sustenance. Place-and community-based forms of surveillance are important for growing an equitable evidence base that integrates climate, food, and health information as well as informs our understanding of how climate change impacts health through local and Indigenous subsistence food systems. METHODS: We present a case-study from southwestern Uganda with Batwa and Bakiga subsistence communities in Kanungu District. We conducted 22 key informant interviews to map what forms of monitoring and knowledge exist about health and subsistence food systems as they relate to seasonal variability. A participatory mapping exercise accompanied key informant interviews to identify who holds knowledge about health and subsistence food systems. Social network theory and analysis methods were used to explore how information flows between knowledge holders as well as the power and agency that is involved in knowledge production and exchange processes. RESULTS: This research maps existing networks of trusted relationships that are already used for integrating diverse knowledges, information, and administrative action. Narratives reveal inventories of ongoing and repeated cycles of observations, interpretations, evaluations, and adjustments that make up existing health and subsistence food monitoring and response. These networks of local health and subsistence food systems were not supported by distinct systems of climate and meteorological information. Our findings demonstrate how integrating surveillance systems is not just about what types of information we monitor, but also who and how knowledges are connected through existing networks of monitoring and response. CONCLUSION: Applying conventional approaches to surveillance, without deliberate consideration of the broader contextual and relational processes, can lead to the re-marginalization of peoples and the reproduction of inequalities in power between groups of people. We anticipate that our findings can be used to inform the initiation of a place-based integrated climate-food-health surveillance system in Kanungu District as well as other contexts with a rich diversity of knowledges and existing forms of monitoring and response.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 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".