What Is a Sikh Museum? Why Sikh Museums?
Bibliographic record
Abstract
Collections of objects of Sikh history and Sikh art exist in the hands of both private individuals and institutions. The most famous examples of private collections include those of the maharaja s of Patiala and Nabha in India, the Kapany Collection and the Khanuja Family Collection in the USA (Taylor and Dhami 2017) and the Toor Collection in the United Kingdom (UK). A selection from the Khanuja family's private collection is now displayed in a dedicated gallery in the Phoenix Art Museum in Arizona, USA (Taylor 2022), and, similarly, a part of the Kapany Collection is housed in the Montreal Museum of Fine Arts in Montreal, Canada. Recently, in 2022, the Lahore Museum in Pakistan inaugurated a Sikh Gallery with objects from the time of Ranjit Singh (Ahmed 2022). The items in these collections range from handwritten and illustrated manuscripts (including of the Guru Granth Sahib ), miniature paintings, sculptures, clothes, weapons, jewellery, coins, pieces of furniture—mostly associated with the court of Maharaja Ranjit Singh and the colonial period, including paintings done or commissioned by colonial officials and early photographs of the Sikhs and their shrines (c. mid-nineteenth to early twentieth centuries). The Sikh Gallery at Lahore Museum, for example, displays portraits of the members of the royal family (of Ranjit Singh), administrative records of the court and even personal items like prayer beads of the maharaja . Illustrated folios of a nineteenth-century Janamsakhi are among the paintings available in the Kapany Collection. Some collections also include modern art by Sikh artists such as the UK-based Singh Twins and some of the artists whose works were discussed earlier in the book (such as Sobha Singh, Jarnail Singh, R. M. Singh and Devender Singh).
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".