Re-examining the value of infrastructure to support urban growth and liveability
Bibliographic record
Abstract
Urban infrastructure systems form an essential component of the urban built environment to \nsupport flows of goods, people, information and resources, and diverse social and economic \nactivities. Growing cities face an imperative to expand infrastructure service provision to \naccommodate greater populations, while also catering for liveability by providing for users’ \nneeds and environmental sustainability. This thesis examines the ways in which infrastructure \nrelates to growth and liveability, and the potential for infrastructure to be instrumentalised as a \ntransformative means to support liveability while accommodating growth. An interdisciplinary \napproach is adopted, utilising qualitative content analysis, spatial econometrics and spatial growth \nanalysis. These methods examine the ways in which infrastructure supports liveability and growth, \ntesting existing agglomeration theory, planning paradigms and spatial strategies. Analysis of \nthe instrumentalisation of infrastructure within spatial planning found that a more thorough \nconsideration of how value is generated from infrastructure, and diverse types of value, can support \ntransitions to improve liveability. Econometric analysis showed that infrastructure mediates \nsocio-spatial relations, enabling agglomeration externalities to be generated across small spatial \nscales. Analysis of urban growth in Auckland, Melbourne and Vancouver revealed that the \n‘compact city’ model for sustainable transitions does not consistently hold true, and supported \nalternative strategies which focus on flows generated, instead of the urban built environment. \nBy examining urban infrastructure from multiple disciplinary perspectives, this thesis develops \na more rigorous understanding of the value derived from infrastructure provision, to inform \ndecision-making. Infrastructure planning can support growth and liveability by refocusing the \nframe of decision-making to the broader socio-technical system in which physical infrastructure \nsystems are embedded. The uncertainty of urban growth implies that investment decisions may \nconsider alternative interventions with lower requirements for physical capital expansion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".