Prioritizing climate adaptation at the local level in Ghana
Why this work is in the frame
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Bibliographic record
Abstract
<abstract> <p>The increasing intensity and frequency of climate impacts exacerbate pressures on front-line local communities. This calls for location-specific adaptation strategies. Alignment of strategies with respective National Climate Change Strategy is key for the overall sustainability of initiatives and local communities. The work presented in this paper examines the adoption and prioritization of climate adaptation policies at the local level based on a case study of the Adansi North District (AND) in Ghana. An assessment of the extent to which climate adaptation policies are captured and budgeted for was done via a review of the district’s medium-term development and key political actors were interviewed to assess the level of priority they place on climate adaptation. Findings from the study reveal that 41% of the locally adopted policies directly align with stipulated national level policies. We attribute the adoption of climate policies in AND to local political actors having higher education which has afforded them good understanding of the climate change phenomenon, being experienced professionals and having to work within institutional rubrics that make climate policy formulation a requirement. However, little priority is given to these policies for implementation, mainly through the non-allocation of funds. We account for this with the weak environmental advocacy in the district and exchange between actors on adaptation. Furthermore, partisan actors who already wield veto powers and can promote policies that may not necessarily support adaptation measures, often do so, since their interest is to become popular among electorates who also prefer infrastructure over environmental policies. We conclude that although climate adaptation policies are fairly adopted and budgeted for in AND, they have not received commensurate priority for implementation. Recommendations are proposed for addressing this.</p> </abstract>
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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