Ethnic Group Recruitment in the Indian Army: The Contrasting Cases of Sikhs, Muslims, Gurkhas and Others
Bibliographic record
Abstract
rT nhe Indian army is one of the largest in the world, with a history going back several hundred years. Several historical works about the army have been written, both by professional soldiers and academics. Some attention has been paid by military historians to the question of class or caste/ethnic/religious/regional group composition of the troops and officers of the army during the colonial period. What is lacking, however, is a systematic account of the ethnic group recruitment to the army since independence and the related questions of the following order: What, historically, is the pattern of recruitment in the Indian army? What changes and continuities with previous policies are discernible? What is the current recruitment policy? Does the composition of the military personnel mirror the religious and ethnic diversity of the Indian national population? If so, to what extent over time? If not, why not and to what extent? Does the military attempt to inculcate national values and perspectives in recruit training and professional military education? Do common military training, corporate life in a highly disciplined environment, isolation in cantonments, and shared experiences serve to reduce ethno-religious identification by building ethnic cross-pressures? Is there trans-community deployment of military personnel? Are promotion decisions based on perceived competence rather than on ethno-religious affiliation? Finally, what is the impact of the polarization of Indian society along the religious divisions of Hindu, Muslim and Sikh, particularly during the last two decades? This paper attempts to answer these questions based on the conversations and writings of military officers, and the published accounts of defence ministers, politicians and informed journalists.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".