Bibliographic record
Abstract
In the early 1980s, separatist pressure in the prosperous agricultural heartlands of the Punjab seemed briefly to threaten the survival of the Indian nation-state. Sikh followers of a charismatic leader, Sant Jarnail Bhindranwale, engaged in local skirmishes with Indian police and soldiers in pursuit of their campaign for an autonomous state of Khalistan. When Bhindranwale and his supporters took refuge in the holiest Sikh shrine, the Golden Temple in Amritsar, Indira Gandhi authorized the use of heavy force to dislodge them. Bhindranwale and many of his supporters were killed in intense fighting, in which parts of the temple complex were also badly damaged. A few months later, Mrs Gandhi was assassinated by her two Sikh bodyguards, provoking waves of attacks on Sikhs in cities across North India, most of them organized by local Congress Party leaders. There followed a decade of escalating violence, in the Punjab and beyond, before the situation gradually calmed in the second half of the 1990s. As with the parallel case of northern Sri Lanka, many of the most dedicated supporters of Khalistan came from the extensive Sikh diaspora, especially in Britain, Canada, and the United States. A few years ago, the American anthropologist Cynthia Keppley Mahmood published an interesting book, based on her conversations with Sikh militants committed to the Khalistan movement. Much of the book is concerned to report, in as authentic a manner as possible, the self-descriptions, personal histories, and political vision of a group of people often labelled as ‘terrorists’, and subject to villification in both popular and academic media.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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".