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Record W4206960778 · doi:10.2307/j.ctv282jfmj

New Developments in Urban Governance

2022· book· en· W4206960778 on OpenAlexaboutno aff
Jonathan S. Davies, Ismael Blanco, Adrián Bua, Ioannis Chorianopoulos, Mercè Cortina-Oriol, Andrés Feandeiro, Niamh Gaynor, Brendan Gleeson, Steven Griggs, Pierre Hamel, Hayley Henderson, David Howarth, Roger Keil, Madeleine Pill, Yunailis Salazar, Helen Sullivan

Bibliographic record

VenueBristol University Press eBooks · 2022
Typebook
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceEnvironmental planningGeographyPolitical scienceBusiness

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.538
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.215
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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