Using Scenario Building and Participatory Mapping to Negotiate Conservation-Development Trade-Offs in Northern Ghana
Bibliographic record
Abstract
In multifunctional landscapes, expanding economic activities jeopardise the integrity of biodiverse ecosystems, generating conservation-development trade-offs that require multi-stakeholder dialogue and tools to negotiate conflicting objectives. Despite the rich literature on participatory mapping and other tools to reveal different stakeholder perspectives, there is limited evidence on the application of such tools in landscape-scale negotiations. This paper addresses this gap by analysing a participatory mapping process in Ghana’s Western Wildlife Corridor, where a community-based landscape governance system called the community resource management area (CREMA) exists. Data from three participatory mapping workshops and focus group discussions with community and institutional actors reveal that increasing demand for food and natural resources and climate change impacts are drivers of landscape degradation, resulting in declining faunal and floral biodiversity and reduced ecosystem services. Meanwhile, community actors prioritise the expansion of farming land, while institutional actors prioritise forest conservation. However, scenario building and participatory mapping helped communicate each other’s aims and reach a negotiated consensus. Finally, power relations, cultural and traditional rules, and differences in knowledge affected deliberations and decision-making. We conclude that scenario building and participatory mapping can contribute to an inclusive landscape approach, provided that well-functioning multi-stakeholder platforms are in place and facilitators adequately navigate power imbalances and recognise different kinds and degrees of knowledge.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".