Bibliographic record
Abstract
The smart city is the most emblematic contemporary expression of the fusion of urbanism and digital technologies. Critical urban scholars are now increasingly likely to highlight the injustices that are created and exacerbated by emerging smart city initiatives and to diagnose the way that these projects remake urban space and urban policy in unjust ways. Despite this, there has not yet been a comprehensive and systematic analysis of the concept of justice in the smart city literature. To fill this gap and strengthen the smart city critique, we draw on the tripartite approach to justice developed by philosopher Nancy Fraser, which is focused on redistribution, recognition, and representation. We use this framework to outline key themes and identify gaps in existing critiques of the smart city, and to emphasize the importance of transformational approaches to justice that take shifts in governance seriously. In reformulating and expanding the existing critiques of the smart city, we argue for shifting the discussion away from the smart city as such. Rather than searching for an alternative smart city, we argue that critical scholars should focus on broader questions of urban justice in a digital age.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.079 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".