What is the Value of Value for Agrarian Studies?
Bibliographic record
Abstract
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?
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.093 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".