Bibliographic record
Abstract
In the early 1890s, Master Tara Singh (Nanak Chand) was so impressed by the stories of Singh martyrs that he thought of becoming a Keshdhārī Singh. Initiated by Sant Attar Singh in 1901, Master Tara Singh decided to dedicate his life to the service of the Sikh Panth. After the government took over the management of Khalsa College, Amritsar, he began to participate in all anti-government agitations. As Head Master of Khalsa High School, Lyallpur, he was closely associated with the group of Sikh leaders who were more radical than the Chief Khalsa Diwan. His sympathy with the ‘Canadian’ Sikhs, and his interest in the <italic>Komagata Maru</italic> voyage and the Budge Budge firing made him all the more anti-British. His familiarity with <italic>gurbāṇī</italic>, Sikh history, and Punjabi literature was reflected in his controversy with the Arya Samaj leaders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".