Developing Climatic Design Guidelines for Australia's Growing Regional Centres: Proposing an Interdisciplinary, Place-based Methodology
Bibliographic record
Abstract
This chapter outlines a novel, interdisciplinary, place-based methodology that was used to develop a climate-responsive building design guide for the Toowoomba region, in Queensland, Australia. There were several contributors to the project outcomes, and this chapter is specifically concerned with the rationale behind the formation of the interdisciplinary team and the project methodology. We present this methodology as novel, not only because it illustrates an alternative type of design contribution for architects – a contribution that was made possible by combining climate science, graphic design, and professional communications with built environment expertise – but because the methodology can inform approaches to climate-resilient and climate-responsive planning policy for other regional urban centers in Australia, and worldwide. In this sense, the project in Toowoomba is presented as a case study for any regional center located outside a major city. This development pattern is an international phenomenon, for example, there are similar trends in Canada, where rural communities exist on the periphery of most major cities, and are experiencing demographic growth, increasing property demand, and “amenity to landscapes” seen to be an attractive alternative to city living. John Friedman highlights that these communities face climatic issues that are both territorially and regionally specific, but also more widespread, presenting particular challenges for local governments in developing appropriate planning policies with limited access to data and resources. Therefore, our task was, first, to collect the weather data needed to define the local climate and to analyse Toowoomba’s local architectural character. Only then, in collaboration with local government and the community, could we engage in a place-based approach (PBA) to research, in which the “essential heterogeneity of rural communities and territories” was foregrounded
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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 | 0.030 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".