Towards socially sustainable urban design: Analysing actor–area relations linking micro-morphology and micro-democracy
Bibliographic record
Abstract
The social sustainability of cities is increasingly assisted by smart apps, social media and the awareness of how social interactions relate to urban space. Within cities, communities or neighbourhoods are no longer easily spatially defined. Similarly, how a community might govern itself does not necessarily follow traditional, simple, spatially self-contained loci. The role of housing management companies, managing a portfolio of social and private housing, adds additional complexity to relations between individual properties and their collective governance, at a level below that of the local municipality. meanwhile, the advent of online crowdsourcing and crowdfunding poses new challenges about the influence of outsiders and ‘who gets a vote’—and who uses their vote—when making decisions about a neighbourhood’s future. This poses a number of challenges for planning and local democracy in the smarter city. This paper reports on new research from the Incubators of Public Spaces project, involving the use of a novel online design and crowdsourcing platform as an experimental tool for public participation, in the case of a london housing estate. In particular, this chapter analyses relationships between different actors and instruments involved in the governance of the different areas or territories of the housing estate. We report on the challenges of holistically engaging a focused yet diverse pool of users in the regeneration of a series of courtyards associated with social housing blocks. This involves non-trivial decisions about user access rights within the platform, which becomes a challenge of reinventing a micro-scale democracy. by modifying standard approaches to social network analysis, the paper develops and demonstrates visualisation of the socio-spatial relationships, linking actor networks and area structures, applied in a novel way to a site’s micro-morphology. This research, yet in progress, can help inform a new generation of planning procedures for more equitable, inclusive and hence socially sustainable cities.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".