New Developments in Urban Governance
Bibliographic record
Abstract
The 2008-2009 Global Economic Crisis (GEC) created an opportunity, eagerly seized by many national governments and international organisations, to impose a prolonged, and widespread period of austerity. Austerity is widely recognised to have done enormous damage to social, cultural, political and economic infrastructures in cities and larger urban areas across much of the globe. As the GEC was also the first such crisis in what is widely considered “the urban age”, (COVID-19 merely the latest and worst), austerity measures were chiefly administered through municipal and regional mechanisms. A great deal has been written since the crisis, about the way austerity was experienced, governed, resisted and urbanised. This volume considers these issues anew, by reflecting on the multi-faceted and shape-shifting concept of “collaboration”. It reflects on the theme of collaborative governance, considered from the perspective of resisting austerity, or otherwise finding ways to circumvent or move beyond it. The insights we draw about collaboration are directed towards locating agency found or created in urban arenas, for resisting or transcending austerity. The book draws on insights into austerity governance from comparative research conducted in Athens, Baltimore, Barcelona, Dublin, Greater Dandenong (Melbourne), Leicester, Montreal and Nantes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".