Climate-resilient development planning for cities: progress from Cape Town
Bibliographic record
Abstract
There is a narrow and closing window of opportunity to shift urban pathways towards development futures that are more climate-resilient and sustainable. This is particularly important for cities implementing local-level climate action together with urgent developmental and sustainability concerns. Climate-resilient development (CRD) is a process of implementing climate action, including greenhouse gas mitigation and risk reduction adaptation measures, to support sustainable development for all 1 . Pursuing CRD involves considering a broader range of sustainable development priorities, policies and practices, as well as enabling societal choices to accelerate and deepen their implementation making climate action and sustainable development interdependent 2 . While prevailing development pathways do not advance climate-resilient development, the Intergovernmental Panel on Climate Change (IPCC) has identified four dimensions that enable progress towards higher climate-resilient development, including equity and justice, inclusion, knowledge diversity and ecosystem stewardship 2 . For example, without progress towards reduced inequality, development cannot be considered climate resilient 3 . Consequently, CRD emphasises the notion of inclusion as a fundamental characteristic of economies, gender, and governance 1 , 4 .
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".