Learning from Community-Based Natural Resource Management (CBNRM) in Ghana and Zambia: lessons for integrated landscape approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Land use in much of sub-Saharan Africa is dominated by legislative frameworks based on a strong colonial legacy, focusing strongly on state control and minimal devolution of management responsibilities to local communities. However, attempts to reconcile conservation and socio-economic development by increasing stakeholder engagement in community-based natural resource management (CBNRM) have been undertaken since the late 1980s. Based on a review of published literature on historical land-use trajectories, the evolution of CBNRM, and key respondent interviews with NRM experts in Ghana and Zambia, this paper asks: What lessons can be learned from CBNRM to inform integrated landscape approaches for more equitable social and ecological outcomes? The paper discusses the positive characteristics and persistent challenges arising from CBNRM initiatives in both countries. The former being, improved rights and resource access, an established institutional structure at the local level, and a conservation approach tailored to the local context. The latter include the absence of multi-scale collaboration, inadequate inclusive and equitable local participation, and limited sustainability of CBNRM initiatives beyond short-term project funding timelines. The paper argues that integrated landscape approaches can address these challenges and improve natural resource management in Ghana and Zambia. We urge landscape practitioners to consider how the lessons learned from CBNRM are being addressed in practice, as they represent both challenges and opportunities for landscape approaches to improve natural resource management.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it