Community engagement in nature-positive food systems programming and research in East and Southern Africa: a review
Bibliographic record
Abstract
There is strong and rapid momentum in the international conservation space toward “nature-positive” programs in development spaces. These food programs and/or interventions exist in a variety of social and geographic contexts. Alongside the movement toward nature-positives systems is a need to work toward social justice, community engagement, and collaboration in the delivery of new programs and approaches. To support the transition and future research in this area we conducted a scoping review that focused regionally on East and South Africa. The focus on these regions was determined based on their long histories of food programs and increasing attention on new nature-positive food programming. Key findings from our scoping review include clear links between actors at different regional scales, such as international agencies, research institutes, governments, and local NGOs; significant barriers to community engagement opportunities in nature-positive food systems seem to fit within three distinct categories (social, environmental, and political/strategic); and lastly, we find that the outcomes of nature-positive food system programs include benefits for the natural environment (i.e., environmental conservation and management, wildlife conservation, and soil quality improvement) as well as benefits for local communities (i.e., alleviating social issues such as poverty, food insecurity, and gender inequality).
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".