How to differentiate peasant classes in capital‐intensive agriculture?
Bibliographic record
Abstract
Abstract This paper highlights the relevance of Marxian class analysis to understand the changing nature of agrarian classes under capital‐intensive agriculture. It is a methodological exercise that builds on Patnaik's labour exploitation index (E‐criterion) in three major respects to construct a new index, namely, the Modified Labour Exploitation Index (MEI), to differentiate peasant classes. First and most important, it incorporates the role of mechanisation, which, so far, has been ignored in the methodological attempts to differentiate within the peasantry. Second, it underscores the importance of non‐agricultural (and non‐rural) bases of simple reproduction in the countryside by incorporating hired‐out labour by agricultural households to the non‐agricultural sector into the classification criteria. Finally, it makes surplus labour exploited through land leasing empirically testable by using Marx's differential and absolute rent to differentiate between subsistence and commercial leasing. The new index is then empirically tested using primary data collected from rural Haryana, India. The paper argues that MEI is an effective criterion for understanding changing class dynamics, the shifting modes of the livelihood of the poor peasantry and the largely hidden accumulation processes in agrarian societies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".