Bibliographic record
Abstract
Governance has been a key concept in urban studies since the late 1980s. This paper reflects on its use and development over the past 25 years and identifies contemporary innovations and concerns that will likely define the future of urban governance studies. The paper argues that to fully understand the impacts of governance approaches on our understanding of cities, urban regions and global urbanism, we must address how urbanism, rather than urbanisation, is governed. An attention to urbanism highlights a wider range of scholarly work on how the mutually constitutive relationships between the development of built environments and the identities, practices, struggles and opportunities of everyday social life are governed. In introducing 15 contributions from the archives of Urban Studies, the paper employs a heuristic framing – urban governance studies (UGS) 1.0, 2.0, and beyond – to show that, while governance as a contemporary critical concept gained prominence through the work of Marxian political economists concerned largely with urbanisation (UGS 1.0), other work, analysing the governance of other aspects of urbanism, including identity and citizenship (UGS 2.0), also has a significant history. The paper then points to ways in which urban governance studies grapple with future-defining challenges, such as climate change, and new framings, such as the ‘smart city’, while extending the scope of their analyses both temporally and spatially. The paper concludes by pointing to gaps and potential topics for ongoing attention.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".