Inevitable Lives: Connecting Animals, Caste, Gender, and the Environment in Perumal Murugan’s <i>The Story of a Goat</i>
Bibliographic record
Abstract
Perumal Murugan’s The Story of a Goat is a nuanced story of a small, black, female goat who endures life and the inevitability of death amongst struggling farmers in Tamil Nadu. Naisargi Dave’s concept of inevitability frames this article, and I explore how Murugan’s novel responds to her question “does that which is inevitable cease to matter?” I argue that the novel resists the inevitability of Poonachi’s death by creating a story of her life that foregrounds her animalness, draws connections between her experience of the world and caste and gender-based oppressions, and presents the natural world as both abundant and a homecoming for Poonachi, as well as dangerous because of the impending drought that looms over the narrative. The Story of a Goat situates animals as beings who are subjects of and subjected to human politics, are made vulnerable to human geopolitics, and have complex stories and histories of their own. The novel offers a compassionate, insightful glimpse into the life of a farmed animal in South India while also tethering her to caste, gender, and the environment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".