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Record W7128508357 · doi:10.64903/1480-6800.20.2.155

The Role of Museums in Correcting the Distorted Image of Islam: A Comparative Study between Qatar and Singapore

2017· article· W7128508357 on OpenAlexvenueno aff

Bibliographic record

VenueArab world geographer · 2017
Typearticle
Language
FieldSocial Sciences
TopicHalal products and consumer behavior
Canadian institutionsnot available
Fundersnot available
KeywordsIslamExhibitionPoliticsHarmony (color)Space (punctuation)Focus group

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.333
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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