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Record W4312059529 · doi:10.1016/j.crm.2022.100471

Climate-driven risks to peace over the 21st century

2022· article· en· W4312059529 on OpenAlexaff
Halvard Buhaug, Tor A. Benjaminsen, Elisabeth Gilmore, Cullen S. Hendrix

Bibliographic record

VenueClimate Risk Management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsCarleton University
FundersHORIZON EUROPE European Research CouncilEuropean Research CouncilEuropean Commission
KeywordsClimate changeAdaptation (eye)Scale (ratio)Global warmingEnvironmental resource managementEnvironmental ethicsPolitical scienceEnvironmental planningEnvironmental sciencePsychologyGeographyEcology

Abstract

fetched live from OpenAlex

Anthropogenic climate change is commonly characterized as a threat to human security. However, the extent to which and under what conditions climate impacts and responses may produce severe risks to peace have seen less systematically assessment to date. This essay provides a conceptual discussion of what risks to peace entail and how such risks might be considered severe, acknowledging that perceptions, values, and social scale must be grappled with in the identification of severity. Informed by available empirical research, the essay then explores the conditions under which climate-related risks could become severe during this century. Three illustrative scenarios based on different assumptions about climate-driven risks and risks related to social responses to climate change serve to illustrate how alternative warming and adaptation trajectories will have distinct implications for the prospect of future peace. The essay ends by reflecting on some implications for future research needs.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.012
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0040.003
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 head0.023
GPT teacher head0.297
Teacher spread0.273 · 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 designSimulation or modeling
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

Citations67
Published2022
Admission routes1
Has abstractyes

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