How elements of the inner city environment can be developed to promote liveability
Bibliographic record
Abstract
New Zealand is one of the most highly urbanised countries in the world with well over 87 per cent of us living in 138 recognised urban centres, yet the number of people residing in inner city areas is proportionally very low. Householders have been exercising their preference for suburban or rural areas by opting for low density suburban environments. It is widely agreed that productivity and sustainability increase when people aggregate in the inner city, however there is a perceived trade-off between the density and liveability of an area. Achieving liveability in the inner city is concerned with reducing the pressures which emerge from higher population densities. Promoting inclusive societies, revitalising underutilised cityscapes, ensuring accessibility and fostering sense of place, are all elements essential to achieving liveable communities. The rebuild following the 2010 and 2011 Canterbury earthquakes provides Christchurch with an opportunity to shape a more environmentally sustainable, economically vibrant and liveable city. This research involves undertaking a case study of current inner city liveability measures and those provided for through the rebuild. A cross-case analysis with two of the world’s most liveable cities, Melbourne and Vancouver, exposes Christchurch’s potential shortcomings and reveals practical measures the city could implement in order to promote liveability.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".