Network mapping of housing systems: The case of medium-density dwelling design in Australia
Bibliographic record
Abstract
Planning and development policies in numerous Australian cities promote consolidation and intensification of activity in existing urban areas. These policies respond to the reducing availability of land for urban expansion, the need to increase infrastructure efficiency, and the desire for a more equitable and sustainable urban future. The medium/high-density housing typologies proposed by such consolidation are readily visualised in the policy documents and sit well within the design capabilities of the local architectural industry. However, these typologies challenge existing Australian housing provision and systemic change is arguably required to enable the outcomes prescribed by the planning and development policies. \nThe vast majority of existing housing provision is low density, with medium/high-density dwellings viewed as contrary to the ‘Australian Dream’. The 2011 Australian census shows three quarters of Australian occupied, privately-owned houses are free-standing suburban dwellings (Australian Bureau of Statistics 2013). Of these, 77% are owner-occupied (Troy 2012) with the remainder being privately rented. This rate of home ownership has been relatively constant since the post WWII period of suburban expansion and increased household mobility. In contrast, privately-owned multi-unit housing (one quarter of the national stock) has a significantly lower owner-occupier rate of just one third (Troy 2012). Hence, for every one owner-occupied dwelling there are two tenanted dwellings, which are characterised by high rates of relocation. Only 13% of people in rental housing are likely to reside at the same address as they did five years prior compared to 71% of owner-occupiers (Australian Bureau of Statistics 2010). These tenure and mobility differences between low density and medium/high-density housing have steered the evolution of two distinct provision systems over time. The resultant built form perpetuates the entrenched perception of medium/high-density housing as an inferior housing alternative to be used as a stepping-stone to the ‘Australian Dream,’ and as an undesirable housing type to have in one’s neighbourhood due to high rental rates. Until such time as this perception is transformed, the planning policies promoting consolidation have limited chance of success and public objections to modifications of existing urban areas are likely to continue.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".