The Role of Museums in Correcting the Distorted Image of Islam: A Comparative Study between Qatar and Singapore
Bibliographic record
Abstract
The paper analyses attempts and efforts in Qatar and by the Muslim community in Singapore to showcase a comprehensive picture of Islam at cultural institutions, museums, and galleries. The aim of such efforts is to reach out to interact with other faiths and cultures and engage in fruitful dialogue that could correct distorted images of Islam and Muslims, a form of ‘cultural diplomacy’. Such attempts have led the Harmony Centre in Singapore (a focus in the article) and the Museums Authority in Qatar (a second focus) to become more vocal, taking a stand on global and other events and abuses. These institutions have started to think of themselves as active agents for social awareness and also seek ever more to address political issues in their exhibitions and programmes. In the process, museums and galleries have become a more vocal space for speaking out and educating about relevant issues relating to Islam and its imaging, and to mutual tolerance between members of different faiths. Cultural presentations, interfaith dialogue, and individual relationships are vital to achieving these ends, and may also have significant political effects, enhancing Muslim communities' soft power. Singapore and Qatar, both small countries, aspire to become global actors, and are using museums as a global showcase for diplomatically chosen aspects of Islamic ethics and principles, and a more comprehensive picture of a tolerant and peace-loving Islam.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".