Class Dynamics of Tenancy and Accumulation in Capitalist Agriculture in India
Bibliographic record
Abstract
ABSTRACT This article underscores the crucial role of tenancy practices in shaping agricultural production dynamics and accumulation/livelihood strategies across different peasant classes in India. By examining tenancy dynamics in Haryana, a key region in India's Green Revolution and forefront of neoliberal reforms, the paper makes two contributions to the literature: First, it contributes to tenancy literature by reinstating Marxist class analysis and second, by employing a novel economic class‐based classification of households rather than traditional size‐based classification. The findings of the study reveal significant variations in objectives and extent of tenancy among economic classes: poor and small peasants engage in hunger (subsistence) leasing due to economic distress, while rich peasants lease for accumulation, leveraging their resources and hired labour. The escalating significance of tenancy amidst agrarian distress in rural India emphasizes that tenancy exploitation persists despite neoliberal reforms. This supports the advocacy of left‐wing parties in India for comprehensive land reforms aimed at redistributing land to actual cultivators, thereby addressing inequities in land ownership and tenancy systems to promote equitable distribution of agricultural resources and alleviate rural poverty.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".