African futures: a review of scenarios for Indigenous and local people and nature in Africa
Bibliographic record
Abstract
There is global understanding of the use of scenarios in addressing continued environmental change in Africa. Scenarios are a powerful tool for exploring uncertainties posed by the Anthropocene. As such, there are increasing calls for the use of scenarios in participatory research to inform policy and decision making. However, very limited research has tackled the integration of Indigenous and local people in participatory scenario planning. This study is an attempt to review knowledge on existing research involving Indigenous and local people in scenario planning in Africa. To do so, we undertook a semi-systematic review of scenario planning for people and nature in Africa of 68 case studies. We found that most of the research on participatory scenarios for people and nature in Africa is undertaken and led by researchers affiliated with institutions outside of Africa and there is a lack of active participation of Indigenous and local communities (IPLC). Of those studies conducted, agriculture and economics are the most common topics covered in the scenarios developed. The findings from this study call for more integration of Indigenous peoples and local communities with their associated knowledge in visioning processes and scenario development and a more inclusive approach to working with researchers based on the African continent for enhanced agency, ownership, and access.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".