A HOME THAT GROWS WITH YOU: Enabling owner-agency through incremental housing in Pakūranga
Bibliographic record
Abstract
What if urban regeneration began with the resident, not the developer? This thesis proposes a new model for suburban transformation in Aotearoa, one that gives residents the tools to shape their own homes, neighbourhoods, and futures. It moves beyond fixed, developer driven housing toward an approach grounded in incremental growth, long term adaptability, and collective agency. Set within the context of Pakūranga Town Centre, the project responds to the social fragmentation and commercial decline brought about by large scale infrastructure upgrades. Through the integration of incremental, modular housing typologies and a responsive master plan, it offers a framework that enables community led regeneration over time. While not a return to the past, the project draws from the values often associated with the Kiwi quarter acre dream, autonomy, pride in place, and the ability to shape one’s environment. These ideals are reinterpreted within a denser urban context, where flexibility and participation replace uniformity and control. In doing so, the thesis positions housing as an open ended process that grows with its people.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".