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Record W4410980427 · doi:10.1111/joac.70018

How is Climate Change Changing Agrarian Studies?

2025· article· en· W4410980427 on OpenAlexaff
Kasia Paprocki, Alejandro Camargo, Marcus Taylor, Suhas Bhasme, Megan Mills‐Novoa

Bibliographic record

VenueJournal of Agrarian Change · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsQueen's University
Fundersnot available
KeywordsAgrarian societyClimate changeAgrarian systemPolitical scienceGeographyNatural resource economicsEnvironmental planningEconomicsAgricultureArchaeologyEcology

Abstract

fetched live from OpenAlex

ABSTRACT A range of compelling recent literature highlights how climate change is rewriting the intertwined social and environmental processes that comprise agrarian landscapes. Mainstream reaction has been to double down on technical intensification strategies supplemented by a resolute faith in scientific advancement to reduce vulnerabilities. For critical agrarian studies, however, climate change raises new conceptual and methodological challenges. Has climate change reinforced or undermined existing concepts and frameworks that explain core dynamics of agrarian change? Does agrarian studies as a field of engaged research need to change alongside the climate? In this exchange our contributors consider how anthropocentric climate change requires the field to rethink core analytical categories within agrarian studies. Key questions that the forum addresses include: How does climate change validate and/or challenge the conceptual armoury and normative orientations inherited largely from Marxist‐influenced political economy? What new concepts and theoretical influences will prove helpful in orientating agrarian studies within a changing climate? How do we synthesise these with existing frameworks and concerns? And how does this reformulation change our understanding of the forms and content of resistance within agrarian environments?

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.271
Teacher spread0.193 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations4
Published2025
Admission routes1
Has abstractyes

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