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Record W4295771610 · doi:10.1017/9781009106801

Divided Environments

2022· book· en· W4295771610 on OpenAlexaff
Jan Selby, Gabrielle Daoust, Clemens Hoffmann

Bibliographic record

VenueCambridge University Press eBooks · 2022
Typebook
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsVulnerability (computing)Climate changeMainstreamPoliticsFaminePolitical sciencePolitical ecologyNarrativePolitical economyEnvironmental securityInterpretation (philosophy)Environmental changeEnvironmental ethicsDevelopment economicsSociologyEcologyLawComputer securityEconomics

Abstract

fetched live from OpenAlex

What are the implications of climate change for twenty-first-century conflict and security? Rising temperatures, it is often said, will bring increased drought, more famine, heightened social vulnerability, and large-scale political and violent conflict; indeed, many claim that this future is already with us. Divided Environments, however, shows that this is mistaken. Focusing especially on the links between climate change, water and security, and drawing on detailed evidence from Israel-Palestine, Syria, Sudan and elsewhere, it shows both that mainstream environmental security narratives are misleading, and that the actual security implications of climate change are very different from how they are often imagined. Addressing themes as wide-ranging as the politics of droughts, the contradictions of capitalist development and the role of racism in environmental change, while simultaneously articulating an original 'international political ecology' approach to the study of socio-environmental conflicts, Divided Environments offers a new and important interpretation of our planetary future.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.086
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0090.008
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0860.025

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 head0.022
GPT teacher head0.207
Teacher spread0.186 · 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 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

Citations34
Published2022
Admission routes1
Has abstractyes

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