Institutional perceptions, adaptive capacity and climate change response in a post-conflict country: a case study from Central African Republic
Bibliographic record
Abstract
The Central African Republic (CAR) faces increased vulnerability to climate change because it is a low-income country with low adaptive capacity; a situation that is exacerbated by recent civil conflict. This research analysed the perceptions of decision-makers within, and the response of diverse national, regional and international institutions to the complex challenges of climate change. Results indicate that while awareness of climate change is high, a concrete response is only in the beginning stages. There was a widespread recognition that the poor who depend on subsistence agriculture, and who constitute the majority of the population, would be most affected. Although CAR has low adaptive capacity, networking and connectivity among different institutions increased through the development of its National Adaptation Programme of Action and the REDD+ (Reducing Emissions from Deforestation and Forest Degradation) documents. In order to mitigate climate change and adapt agriculture and natural resource management to long-term trends in climate variability, such linkages need to be strengthened to build capacity within government institutions, within local communities and within non-governmental organizations that work with those communities. Building adaptive capacity to climate change can also contribute to the process of reconstruction, reconciliation and peace building in the country.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".