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Record W2009409620 · doi:10.1080/00045608.2014.973807

Climate Change and the Adaptation of the Political

2014· article· en· W2009409620 on OpenAlexaff
Joel Wainwright, Geoff Mann

Bibliographic record

VenueAnnals of the Association of American Geographers · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsClimate changePoliticsClimate change adaptationAdaptation (eye)Political scienceGeographyEnvironmental resource managementPolitical economyEnvironmental scienceEconomicsPsychologyEcology

Abstract

fetched live from OpenAlex

In the face of climate change, along what path might we attempt transformation that could create a just and livable planet? Recently we proposed a framework for anticipating the possible political–economic forms that might emerge as the world's climate changes. Our framework outlines four possible paths; two of those paths are defined by what is called “Leviathan,” the emergence of a form of planetary sovereignty. In this article we elaborate by examining the adaptive character of emergent planetary sovereignty. To grasp this, we need a theory that can see through our ostensibly “postpolitical” moment to grasp not the disintegration but the adaptation of the political. What does it mean to say the political adapts? Reduced to its essence, it is to say that if the character of political life prevents a radical response to crisis, then it is the political that must change. A materialist attempt to elaborate on this question must begin by reflecting on the manifest inequalities of power in the current mode of global political-economic regulation. After doing so, we conclude by arguing for a return to the concept of natural history.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.026
Scholarly communication0.0050.004
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.248
Teacher spread0.222 · 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 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

Citations33
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the Association of American GeographersSame topicClimate Change and GeoengineeringFrench-language works237,207