Kenya's focus on urban vulnerability and resilience in the midst of urban transitions in Nairobi
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
Addressing urban vulnerability requires an understanding of the underlying determinants of resilience for individuals, households, communities and institutions -- to withstand shocks, to adapt and to change. Analysing urban resilience utilises the results of five rounds of the Indicator Development for Surveillance of Urban Emergencies surveys conducted in three informal settlements of Nairobi. Results show a significant deterioration in food security and household hunger in marginalised urban populations, with other deprivations including insecurity, negative coping behaviour and inadequate access to water and sanitation. Within slum populations, there was a significant variation in income and expenditure (p > 0.05) with lowest income quintiles spending over 100% of their income on food. Significant gender disparities have been shown in lowest income quintiles, with female breadwinners earning 62% compared with male breadwinners (p > 0.05). Recommendations from this analysis include establishing thresholds for vulnerability and concrete dimensions for measuring resilience that can initiate and guide related interventions.
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 | 0.000 | 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