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Record W4403890822 · doi:10.5751/es-15469-290413

Climate–water crises: critically engaging relational, spatial, and temporal dimensions

2024· article· en· W4403890822 on OpenAlexfundvenueno aff
Nicole J. Wilson, Sameer H. Shah, Teresa Montoya, Catherine Fallon Grasham, Marina Korzenevica, Thanti Octavianti, Jaynie Vonk, Farhana Sultana

Bibliographic record

VenueEcology and Society · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsnot available
FundersCanada Research ChairsForeign, Commonwealth and Development OfficeNational Science Foundation
KeywordsEnvironmental resource managementClimate changeGeographyEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Declarations of water crises have been ubiquitous in water policy and practice for decades. In the face of unprecedented human-caused climate change, the circulation of water crisis discourses has increased in frequency. How crises are defined and made meaningful, however, is often assumed to be commonly agreed upon. Reviewing scholarship at the intersections of water and climate, we show that crisis discourses are inherently political because they depend both on the authority and legitimacy to delineate exceptions from norms, and on the powers to mobilize resources to respond to constructions of crisis. Engaging with crisis as an explicitly normative concept helps situate analyses within the social, historical, political, and geographic particularities of water–climate systems. We identify three interrelated analytical frames that assist with this task: relationality, spatiality, and temporality. Our review hopes to better position researchers, policy makers, and activists to critically engage with crisis narratives. Doing so can effectively advance more critical, creative, and imaginative crisis responses.

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)0.023
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0090.030
Scholarly communication0.0170.022
Open science0.0030.013
Research integrity0.0050.006
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 head0.016
GPT teacher head0.242
Teacher spread0.225 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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