Community adaptation strategies in Nairobi informal settlements: Lessons from Korogocho, Nairobi-Kenya
Bibliographic record
Abstract
Informal settlements are often the hotspots of vulnerability as evidenced by the recurrent environmental and climate-related shocks and stressors. Despite this exposure and susceptibility, their role in spearheading disaster risk preparedness and response is often overlooked. This exploratory research profiles four local community initiatives for climate mitigation and adaptation within Korogocho informal settlement in Kenya. Findings from 10 purposefully sampled key informants and 30 stratified sampled residents across nine villages within the informal settlement demonstrated the impact of locally led initiatives in creating awareness and developing the absorptive, adaptive and transformative capacity of communities for climate resilience. The research findings elaborate on the outstanding performance of community derived initiatives, whilst putting emphasis on the need for active dialogue and collaboration between communities, policy makers and practitioners. Additionally, the climate agenda ought to be able to simultaneously promote environmental benefits and the socio-economic wellbeing of the people. This study accentuates the role of smart approaches to climate literacy based on existing community structures that leverage on local experiential knowledge. These include digital storytelling, comics, art, music, local radio stations, community opinion leaders and chief barazas . A key takeaway is the significant role of children in transformative climate resilience. This is facilitated by the fact that they may comprehend climate change implications better than adults augmenting the possibility of human behavioral change toward pro-environmental deeds 1 .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| 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".