Who Does The Dying?: 'Martial Races' and War Time Unit Deployment in the Indian Army
Bibliographic record
Abstract
During the Second World War, the Indian Army held back units and soldiers that were not from the so-called “martial races” from frontline combat service. The British “martial races” theory held that only a small number of communities in India were fit for military service and people from all “non-martial” communities should be excluded from the Army. Has the Indian Army, after gaining independence from British leadership, contended the Second World War practice of deploying “martial” units in combat while assigning “non-martial” units to non-combat roles? It has been conclusively demonstrated that “martial race” groups have contended to be overrepresented in the post-colonial Indian Army. However, there has been little study into how the “martial races” theory has continued to affect the Indian Army outside the matter of the recruitment of soldiers. The Indian Army, while being forced by political leadership to open up recruitment to “non-martial” communities, contended the Second World War practice of tending to select units composed of “martial” soldier to see action while tending to held back units composed of “non-martial” soldiers. Using a new data set I build, this study measures the effect of martial identity on the likelihood an infantry company in the Indian Army will be deployed into combat during a war. During each war sampled, the Kashmir War 1947-8, the Sino-Indian War 1962, the India-Pakistan War 1965, and the India-Pakistan War 1971, “martial” infantry companies were more likely to see combat action then “non-martial” units. However, this effect is heterogeneous across the wars, with greater effect seen in the Sino-Indian War 1962 and India-Pakistan War 1971. This thesis contributes to the literature by advancing a new theoretical understanding of the political consequences of social representation in combat fatalities. Groups that fight for the country can demonstrate a “blood sacrifice” for the nation. A group’s political leadership can use the memory of that “blood sacrifice” to make demands from the political system. However, there are barriers to a group’s ability to make demands from the political system that may not be overcome by the memory of “blood sacrifice”.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".