Bibliographic record
Abstract
As long as population growth continues, policies for urban consolidation closer to city centres fail, and there is land available, Australians will continue to build in new Greenfield suburbs.However, the 50-year legacy of the homogeneous one-size-fits-all approach to suburbia beyond the sticks and sometimes hours away from where one can find a job, is proving unsustainable, the commute alone a significant contributor to greenhouse gas emissions across the globe.The 'creative suburb' was inspired by the possibility to create new, innovative and entrepreneurial suburbs, places which are more selfsufficient and self-contained than the 'product' perpetuated down under even today.The 'creative suburb' draws on significant primary research with suburban home-based creative industries workers, vernacular architecture, and town planning in the Toowoomba region, in the state of Queensland, Australia, as inspiration for a series of new building and urban designs available for innovators operating in new suburban greenfield situations in Queensland and possibly further a field.This paper considers the role 'creative reflective practice' played in the process of developing the building and urban designs presented in a book and showcased in a building as creative outputs of this practice-led and property development industry embedded inquiry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.051 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".