Climate Anticipation: working towards a design proposal for urban resilience and care
Bibliographic record
Abstract
The 'winter of…' and the 'hottest day since…' are narratives that describe our experience with a climate that informed our behaviours of the time.What was, isn't necessarily what will be, and as global climate change shifts and pushes us into unfamiliar climatic experiences, we seek a more meaningful way to anticipate climate change.In this instance climate is viewed as a disruptive element in society with its shift, apparent unpredictability, and impact affecting those less equipped to anticipate.With extreme changes in temperature, high air pollution levels, and lack of rain water, climate change is felt and seen.CAPE (Climate Anticipation Personal Environment) is a conceptual framework to inform society of impending environmental extremes by communicating immediate futures.Four case studies explore current technologies being applied in our surrounding terrestrial and extraterrestrial environments.Together they represent our anticipated materialisation of CAPE.This paper seeks to enable vulnerable communities to be better prepared through warning systems, to better seek relief through interventions, and to develop anticipation and care in large cities, those lacking green spaces and natural approaches in order to align climate anticipation with the needs of society.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".