Trojans of ambiguity vs resilient regeneration: visual meaning in cities
Bibliographic record
Abstract
This paper presents a theoretical framework that helps identify visual sustainability in urban projects and evaluates its relevance for the use, design and making of public space. It is aimed at showing how the process of urban regeneration is far more nuanced and sophisticated than much of today’s building industry allows for. The first part of the article provides an outline of this framework, by drawing from the notion of ambiguity and discussing regeneration around a concept of trojans of ambiguity: by which we simply mean that modern-day regeneration projects are often a confusion of meaning. The framework is then applied to two case studies: Heygate, and Sidewalk Labs Toronto. The Heygate regeneration produced a negative emotionally charged process and social displacement. By contrast Sidewalk Labs Toronto exemplifies a technologically clean start for regeneration, on a site with little social vitality or history. The starting points for each ultimately point to two very different outcomes. Visual sustainability represents ‘the technology before the technology’ and future research must recognise how human needs, not technology, provide the meaning into ‘how’ we may create a successful, smart, and sustainable urban environment.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.077 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".