‘Couldn’t we call it something else?’: the Indian Army’s <i>sahayak</i> system and categorizing military labour
Bibliographic record
Abstract
The Indian Army’s sahayak system, named after the Hindi word meaning ‘assistant’ or ‘helper’, formerly the colonial batman or orderly system, recently became a site of national controversy. Enlisted soldiers charged the army with exploitation for being coerced into performing domestic chores in officers’ households. I argue that military practices like the sahayak system show soldiering cannot be neatly classified into universal categories of ‘work’, ‘labour’, or ‘service’, but instead occupy their interstices. Building on analytical approaches within the anthropology of work and feminist studies labour, the article traces the shifting meanings of these categories across scholarly and media debates, colonial propaganda materials, and ethnographic encounters. It shows these categories are not fixed but actively negotiated through boundary-making practices by the subjects who inhabit them and the institutions that invest in and seek to manage them. The analysis of media debates and ethnographic insights is grounded in colonial historiography to illustrate how ideologies of ‘work’ and ‘service’ were shaped and contested within earlier institutional regimes through ‘status lines’ drawn between combatants and followers, and how contemporary boundary-making around the sahayak system echoes these tensions. Additionally, it demonstrates how language itself becomes a site of contestation over how military labour is defined, valued, and made visible. These dynamics emerge as particularly urgent in the current political climate, where resurgent nationalism in India depends on one hand upon romanticized portrayals of soldiering and on the other upon active repression of dissent amongst soldiers protesting their working conditions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".