Injustice in Urban Sustainability
Bibliographic record
Abstract
This book uses a unique typology of ten core drivers of injustice to explore and question common assumptions around what urban sustainability means, how it can be implemented, and how it is manifested in or driven by urban interventions that hinge on claims of sustainability. Aligned with critical environmental justice studies, the book highlights the contradictions of urban sustainability in relation to justice. It argues that urban neighbourhoods cannot be greener, more sustainable and liveable unless their communities are strengthened by the protection of the right to housing, public space, infrastructure and healthy amenities. Linked to the individual drivers, ten short empirical case studies from across Europe and North America provide a systematic analysis of research, policy and practice conducted under urban sustainability agendas in cities such as Barcelona, Glasgow, Athens, Boston and Montréal, and show how social and environmental justice is, or is not, being taken into account. By doing so, the book uncovers the risks of continuing urban sustainability agendas while ignoring, and therefore perpetuating, systemic drivers of inequity and injustice operating within and outside of the city. Accessibly written for students in urban studies, critical geography and planning, this is a useful and analytical synthesis of issues relating to urban sustainability, environmental and social justice. The Open Access version of this book, available at https://www.taylorfrancis.com/books/e/9781003221425, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. Funded by Universitat Autònoma de Barcelona
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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".