Building Urban Resilience in the Post-2015 Development Agenda: A Case Study of Harare, Zimbabwe
Bibliographic record
Abstract
This chapter examines urban resilience building efforts in Harare. The analysis is placed within the urban resilience framework. The Post-2015 development agenda is committed to ‘make cities and human settlements inclusive, safe, resilient and sustainable’ (SDG 11). For this chapter, urban resilience means the ability of a system, entity, community, or person to adapt to a variety of changing conditions and to withstand shocks while still maintaining its essential functions. The four (4) dimensions of urban resilience namely infrastructure, social, economic and institutional resilience are considered. Harare was purposefully selected as it is one of the pilot local authorities under the “Partnership for Building Urban Resilience in Zimbabwe” programme by the UNDP, UNICEF and Ministry of Local Government, whose goal is to improve urban resilience and strengthen the provision of basic social services and Local Economic Development (LED) targeting unemployed youths, women, and vulnerable groups in urban and peri-urban areas (UNDP Urban Resilience Building Programme Document, 2019). The chapter concludes that there are major gaps in urban resilience building in Harare. The City is characterised by under-investment in critical infrastructure, weak urban planning and governance frameworks (including outdated policy frameworks) and lack of climate adaption planning. These factors not only work against urban resilience building, but they also hinder progress towards achieving resilient, inclusive and sustainable urban communities. For effective urban resilience building, Harare needs to prioritise investment in resilient urban infrastructure (water, sanitation, and storm water), research on the vulnerability of cities and towns, internalising global and national frameworks on climate change through climate adaption planning and strengthening urban planning and governance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".