Kenya's focus on urban vulnerability and resilience in the midst of urban transitions in Nairobi
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".