The Madness of Jodh Singh: Patriotism and Paranoia in the Ghadar Archives
Bibliographic record
Abstract
My paper focuses on Jodh Singh, a marginal figure in the archives of the Ghadar Party, who was arrested for High Treason against the United States for his role in the “Hindu Conspiracy” plots aimed at the British government of India. Incarcerated in a California prison, Singh was moved to a sanatarium on displaying symptoms of insanity. Through a close reading of a web of archival documents and scholarly reflections—at the center of which lies the report of a commission appointed to inquire into his mental condition—I examine the account of the madness of Jodh Singh as a statement about patriotism and paranoia. In engagement with the work of Foucault, Guha, and scholars of the Ghadar movement, I describe how the record of Singh’s experiences indicts the juridical-legal-medical framework of American society as operating on a distinction between legtimate and illegitimate madness. I also examine how Jodh Singh points to the glimmers of a critique of the self-image of the Ghadar Party as a revolutionary movement committed to egalitarian principles. I conclude with a reflection on what Jodh Singh might tell us about the relationship between madness, political aspiration, and the yearning for solidarity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.053 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".