Bibliographic record
Abstract
Transformational-scale efforts on the part of the built environment professions are crucial to stave off the gravest scenarios of runaway climate change, and the current policy and architectural and engineering work in even leading green cities will not achieve this transformation. Urban planning and policy has tended to consider a strictly regulatory approach to be the most appropriate way to develop the urban built environment and has offered voluntary programmes to industry leaders seeking to distinguish themselves as innovators above and beyond this regulatory baseline. This chapter presents a new approach to regulation and innovation that offers a more pragmatic alternative in the face of climate emergency. A detailed, design-based regulatory approach to greening the built environment that draws upon a wide range of expertise holds promise of achieving the drastic reductions in greenhouse gas emissions. The case of the City of Vancouver’s design-based approach to urban design and built environment regulation, and its step-wise progression towards more ambitious regulation of innovation towards better green building performance, provide an illustration of the potential of a pragmatic approach to regulating our way to net zero cities. The story represents a contrast case to the predominant bias against regulation that sees regulation as opposed to innovation.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".