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Record W4415207265 · doi:10.1177/13691481251371543

Political theories of climate temporality: Extinction Rebellion, emergency time and decolonial time

2025· article· en· W4415207265 on OpenAlexfundno aff
Tobias Müller

Bibliographic record

VenueThe British Journal of Politics and International Relations · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
FundersUniversität HamburgCentre for Research in the Arts, Social Sciences and Humanities, University of CambridgeUniversità degli Studi di TrentoYale UniversityUniversity of CambridgeIsaac Newton TrustLeverhulme TrustUniversity of TorontoUniversity of Oxford
KeywordsTemporalitiesPoliticsTemporalityClimate changeEthnographyExtinction (optical mineralogy)AnthropocenePolitical economy of climate change

Abstract

fetched live from OpenAlex

The politics of climate change is also a politics of conflicting temporalities. However, we know little about how conceptualizations of climate and time shape political contestations on the ground. Similarly, we lack empirically grounded reflections on how different climate temporalities relate to each other and how they are shaped by histories of colonialism. This article addresses these gaps by developing an analytical framework of climate temporalities that investigates how activists combine diagnosis and proposed action in their political visions. Using ethnographic political theory, the article examines two competing temporalities within the climate movement Extinction Rebellion, namely, emergency time and decolonial time . Based on this analysis, the article argues that we should understand the relationship between different climate temporalities as intersecting, spatialised and resonant. This opens up new avenues for integrating political theory and empirical research into how past, present and future are mobilised for political purposes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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 designTheoretical or conceptual
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

Citations2
Published2025
Admission routes1
Has abstractyes

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