A Quick-Scan Methodology Incorporating Local Knowledge for Climate Risk and Vulnerability Assessments Applied in Kampala
Bibliographic record
Abstract
Abstract A surge in publications on climate change vulnerability and risk assessments in recent years reflects the escalating impacts of climate change. These assessments are crucial for improving climate change adaptation planning. City-level integrated climate risk and vulnerability assessments (CRVAs) are increasingly relevant, particularly for African cities facing heightened vulnerability from local climate impacts and urbanization. There are several practical challenges in the context of Sub-Saharan African cities: 1) research capacity limitations; 2) the heterogeneity of settlements and their infrastructure; 3) data availability and accessibility; and 4) inclusion of local knowledge in the data collection process. This study aims to address these challenges through an integrated quick-scan CRVA approach. The methodology was developed during the COVID-19 pandemic to be conducted on distance with local partners, and tested through an instrumental case study in Kampala, Uganda. The case of Kampala shows the execution and resulting scan, with mapped districts where climate threats are urgent, summarized in so-called ‘neighbourhood profiles’. The method description and its implementation demonstrate that this form of CRVA methodology holds the potential to: (a) expedite city-wide climate assessments; (b) provide a filter procedure and a classification of diverse needs across districts; (c) bring together ‘insider knowledge’ and ‘outsider expertise’ and (d) establish knowledge collaborations across distances and scales. In just a few months’ time, the project team navigated in both informal community systems and formal institutional frameworks. Preferable to the alternative of complete absence of vulnerability assessments, the described ‘quick-scan method’ may be worthwhile for other African cities.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".