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Record W4417164030 · doi:10.1177/20438206251398556

The modalities and politics of crisis urbanism: A new reparative conjuncture?

2025· article· en· W4417164030 on OpenAlexaff
Ross Beveridge, Roger Keil, Maryam Lashkari

Bibliographic record

VenueDialogues in Human Geography · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsYork University
Fundersnot available
KeywordsUrbanismPoliticsTransformative learningModalitiesFraming (construction)Urban politics

Abstract

fetched live from OpenAlex

This article argues that the current advance of global crises necessitates new thinking on how the urban intersects with crisis. We develop four overlapping modalities for theorizing how multiple and deepening crises are entwined with urbanism and are generative of a conjuncture we approach as crisis urbanism : chrono-politics of crisis urbanism, spatial-politics of crisis urbanism, statal-politics of crisis urbanism, and the epistemological politics of crisis urbanism . The theoretical framing of these modalities sheds light on the interlinking and enduring character of crisis urbanism and offers a better understanding of poly- and perma-crises associated with the urban way of life and the political geographies these are generating, including, dialectically, the turn to reparative urbanism to address harms within the city. Crisis is understood as not (yet) enveloping urbanism but rather as an ever-present process within urbanization. Crisis urbanism is, then, the name we give to this process, but it is also a method: a means of analysing an always politically constructed and dialectically composed process, one that is relational and ongoing. In conclusion, the article reflects on the prospects of transformative reparative politics within and beyond the current urban conjuncture.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.085
Scholarly communication0.0190.034
Open science0.0020.017
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0090.001

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 head0.018
GPT teacher head0.300
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2025
Admission routes1
Has abstractyes

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