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Record W4410857680 · doi:10.1111/joac.70020

What is the Value of Value for Agrarian Studies?

2025· article· en· W4410857680 on OpenAlexaff
A. Haroon Akram‐Lodhi, Srishti Yadav, Alessandra Mezzadri, Marcus Taylor

Bibliographic record

VenueJournal of Agrarian Change · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsQueen's UniversityTrent University
Fundersnot available
KeywordsAgrarian societyValue (mathematics)EconomicsPolitical scienceGeographyMathematicsAgricultureStatisticsArchaeology

Abstract

fetched live from OpenAlex

ABSTRACT Reflecting a longstanding intellectual heritage in Marxist political economy, contributions to agrarian studies have variously referred to the production, distribution and extraction of value. Despite this central role within the heritage of agrarian studies, the concept of value is often used inconsistently between authors and sometimes deployed without clear elucidation of the underlying theoretical tenets. As such, value often tends to be used more as a metaphor suggestive of conditions of exploitation rather than a detailed conceptual framework. In response, we must ask if there is still a robust case for value analysis forming a foundational pillar of agrarian studies? To address this challenging question, we invited three authors to give their perspective on the value of value for agrarian studies. First and foremost, we asked them to consider what value analysis does that is otherwise missed in critical agrarian studies and how we can mobilise its potential to sharpen analyses. Two further pivotal questions arise, spurred on by recent trends in the literature. First, to what extent do the categories of value enrich or hinder our evolving understanding of the dynamics of social reproduction within agrarian households and communities, including the gendered relations through which agriculture and livelihoods are performed? Similarly, are the largely anthropogenic concepts of value fit for the purpose of explaining environmental change and the more‐than‐human dynamics through which agricultural landscapes are produced and change over time?

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)0.049
metaresearch head score (Gemma)0.060
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 score0.049
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.060
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0070.093
Scholarly communication0.0260.027
Open science0.0030.010
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0040.000

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 head0.065
GPT teacher head0.293
Teacher spread0.228 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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