Design support tools to sustain climate change adaptation at the local level: A review and reflection on their suitability
Bibliographic record
Abstract
For mid-latitude cities, higher summer temperatures due to climate change are a cause for concern because they aggravate the urban heat island phenomenon and reduce thermal comfort inside buildings. By acquiring the appropriate knowledge and skills, architects and urban designers can become key actors in adaptation to climate change. Two workshops bringing together architects and urban designers provided evidence of deficiencies in this area. We hypothesize that a design support tool (DST) focused on the issue of adaptation of mid-latitude cities to rising summer temperatures could help improve knowledge and skills of professionals in the field. The first section presents the results taken from a review and classification of DSTs, which highlight the tools׳ features that are likely to reach this goal. Tools of the “hybrid” category seem most appropriate. To verify this, seven DSTs were selected and tested by fourteen students enrolled in a graduate-level architecture design studio. The second section presents the results from this test, including an analysis of the final projects, a web-based questionnaire and two focus groups. The relevance of hybrid approaches is established, but the results bring into question the capacity of a single DST to meet the individual and multiple needs of professionals.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".