Institutional adaptive capacity and climate change response in the Congo Basin forests of Cameroon
Bibliographic record
Abstract
Climate change presents additional challenges to a diverse country like Cameroon that shares the Congo Basin rainforest. Not only is the population vulnerable to the direct effects of climate change, forest-dependent communities are also vulnerable to changing environmental policy that may affect their access to forest resources. Using a qualitative approach to data collection through semi-structured interviews and content analysis of relevant documents, the perception of decision-makers within, and the response of the institutions of the state, the private sector and civil society to the complex challenges of climate change in the Congo Basin forest of Cameroon were analysed. Results indicate that while decision-makers’ awareness of climate change is high, a concrete institutional response is at a very early stage. Cameroon has low adaptive capacity that is further constrained by weak linkages among government institutions nationally and between different levels of government and with communities. Civil society institutions play a role in enhancing government capacity to respond, particularly in relation to new international policies on climate change and forests. Adaptive capacity would be further enhanced by facilitating institutional linkages and coordinating multilevel responses across all boundaries of government, private sector and civil society. A collaborative capacity builder could foster the transfer, receipt and integration of knowledge across the networks, and ultimately build long-term collaborative problem-solving capacity in Cameroon.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".