‘Women stay behind and grow the food’: Agricultural productivity and the interstices of petty commodity production and reproductive labour in Tanzania
Bibliographic record
Abstract
Abstract Inspired by the work of Carmen Diana Deere, this paper examines how an analysis of the work of rural production, even when gendered, is compromised if it does not incorporate reproductive labour. The paper presents estimates of the gender yield gap in agricultural crop productivity in Tanzania, along with the statistical causes of the gender yield gap, in order to demonstrate what is and why it matters. The paper then shows that the gender yield gap cannot be understood without interrogating how the reproductive labour of unpaid care and domestic work limits the time for productive activities available to women who have day‐to‐day decision‐making managerial control over plots of land. In this light, the paper suggests a way of rethinking the basic analytical frameworks of agrarian political economy in ways that are consistent with and incorporate the theoretical insights of Carmen Diana Deere. The implications of the analysis are stark: it should not be assumed that all members of an agrarian household share an identical class location, as remains far too often the default assumption in agrarian political economy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".