Bend Like the Grass: Ecofeminism in Kamala Markandaya's Nectar in a Sieve
Bibliographic record
Abstract
This paper revisits an early postcolonial favourite, Kamala Markandaya’s Nectar in a Sieve, by reading it as a possible ecofeminist text. Markandaya’s novel about a peasant farmer and her daily struggles has fallen out of favour, perhaps due to the often dismissive reading of Rukmani, the heroine, as a passive female character, a position which this paper challenges using de Certeau’s concept of tactics and power. Given the troubled history of ecofeminist thinking about women in the global South, this paper asks whether an ecofeminist reading still makes critical sense. The overwhelming role of nature in the novel, however, and particularly in Rukmani’s understanding of the value of life, means that it would be misguided to overlook these ecofeminist themes. This paper, then, aims to situate an ecofeminist reading within the context of a more specific postcolonial analysis in order to understand Rukmani as an active agent through her labour on the land and her embrace of community. The paper in particular focuses on the ways in which Rukmani employs tactics to negotiate modernity through the under-examined relationship between herself and the white doctor, Kenny. Rukmani’s ultimate decision to return to the land is not read as a retreat, but rather a thoughtful response to urbanization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".