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Record W2911964719 · doi:10.1177/0042098019826022

Advanced perspectives on financialised urban infrastructures

2019· article· en· W2911964719 on OpenAlexaff
Heather Whiteside

Bibliographic record

VenueUrban Studies · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCitizen journalismCorporate governanceUrban infrastructureState (computer science)Critical infrastructurePolitical scienceSociologyBusinessPublic administrationUrban planningFinanceEngineering

Abstract

fetched live from OpenAlex

This Special Issue attempts to clarify how urban infrastructure is being funded, financed and governed. In this commentary, I seek to engage the topic of the Special Issue as a whole – infrastructure financialisation and its governance – albeit through examples provided by individual article contributions. It is a collection emphasising the tangled interaction between public and private, urging a view of financialisation beyond the binary states vs. markets, and highlighting the multiple actors with multiple agendas at play. The articles provide richly detailed accounts of how the local state remains active, participatory and deeply – if not daily – involved in infrastructure financialisation, even/especially when finance is at its most influential. Not without its limitations, three occlusions in this Special Issue present opportunities for future research, namely the need to: i) extend critical analyses of financialisation; ii) enhance related research on social infrastructure, operational phase processes and treatment of the global South; and iii) advance academic analysis of alternatives to infrastructure financialisation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.021
GPT teacher head0.237
Teacher spread0.215 · 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.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations39
Published2019
Admission routes1
Has abstractyes

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