Introduction
Bibliographic record
Abstract
The 2008–2009 Global Economic Crisis (GEC) created an opportunity, eagerly seized by many national governments and international organizations, to impose a prolonged, and widespread period of austerity. Austerity is widely recognized to have done enormous damage to social, cultural, political and economic infrastructures in cities and larger urban areas across the globe (Davies, 2021). As the GEC was also the first such crisis in what is widely considered ‘the urban age’ (Brenner and Schmid, 2015), (COVID-19 merely the latest and most intense), 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 urbanized. This volume considers these issues anew, by reflecting on the multi-faceted and shape-shifting concept of ‘collaboration’. It draws from research funded by the UK Economic and Social Research Council titled Collaborative Governance Under Austerity: An Eight Case Comparative Study, led by the Centre for Urban Research on Austerity at De Montfort University in the UK City of Leicester.1 Research was conducted over three years (2015–2018) in the European cities of Athens, Barcelona, Dublin, Leicester and Nantes, North American cities of Baltimore and Montréal, and the Australian City of Greater Dandenong, part of the Greater Melbourne metropolis. Our objective in this volume is to reflect on the theme of collaborative governance, considering this from the perspective of resisting austerity, or otherwise finding ways to circumvent or move beyond it. As a research team, we have a range of political views, but all share egalitarian sympathies articulated in the following chapters.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.498 | 0.327 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".