Aesthetics of Gentrification
Bibliographic record
Abstract
Gentrification is reshaping cities worldwide, resulting in seductive spaces and exclusive communities that aspire to innovation, creativity, sustainability, and technological sophistication. Gentrification is also contributing to growing social-spatial division and urban inequality and precarity. In a time of escalating housing crisis, unaffordable cities, and racial tension, scholars speak of eco-gentrification, techno-gentrification, super-gentrification, and planetary-gentrification to describe the different forms and scales of involuntary displacement occurring in vulnerable communities in response to current patterns of development and the hype-driven discourses of the creative city, smart city, millennial city, and sustainable city.\nIn this context, how do contemporary creative practices in art, architecture, and related fields help to produce or resist gentrification? What does gentrification look and feel like in specific sites and communities around the globe, and how is that appearance or feeling implicated in promoting stylized renewal to a privileged public? In what ways do the aesthetics of gentrification express contested conditions of migration and mobility? Addressing these questions, this book examines the relationship between aesthetics and gentrification in contemporary cities from multiple, comparative, global, and transnational perspectives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".