Comparative infrastructural modalities: Examining spatial strategies for Melbourne, Auckland and Vancouver
Bibliographic record
Abstract
Infrastructure systems are critical to support sustainable and equitable urbanisation, and infrastructure is becoming more prominent within urban spatial strategies. However, the fragmented governance and delivery of spatial plans and infrastructure projects create a challenging environment to embed planning goals across the planning, delivery and operation of infrastructure systems. There is significant uncertainty around future needs and the complex ways that infrastructures influence socio-spatial relations and political-economic processes. Additionally, fragmented knowledge of infrastructure across different disciplines undermines the development of robust planning strategies. Comparative analysis of strategic spatial plans from Auckland, Melbourne and Vancouver examines how infrastructures are instrumentalised to support planning goals. Across the three cases, the analysis identified four common infrastructural modalities: rescaling socio-spatial relations through targeted intensification, intra-urban mobility upgrades and containment boundaries; re-localising socio-spatial relations to the suburban scale with ‘complete communities’; protection of ‘gateway’ precincts; and local planning provisions to support housing affordability. By examining infrastructure through a theoretical framework for suburban infrastructures, this analysis revealed how infrastructures exert agency as artefacts shaping socio-spatial relations and through the internalisation of political-economic processes. Each modality mobilised infrastructure to support goals of global competitiveness, economic growth and ‘liveability’. Findings suggest that spatial strategies should take a user-focused approach to infrastructure to meet the needs of diverse urban populations, and engage directly with the modes of infrastructure project delivery to embed planning goals across design, delivery and operations stages. Stronger institutional mandates to control land-use and provide affordable housing would improve outcomes in these city-regions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".