Bibliographic record
Abstract
It is a great privilege and honor for me to write a foreword to this volumean innovative exploration of the relationship between the aural/sonic arts and the visual/spatial arts in Muslim societies.Comprising contributions from scholars working in an array of disciplines, the collection examines how the sonic arts, such as music, shape and are shaped by the physical spaces in which they are performed.In so doing, it provides us with new perspectives on the dynamic relationship between various forms of art in cultural, sociopolitical, and religious spaces.More important, the volume's essays demonstrate how a multisensory approach-one that combines sound with built structure, music with architecture, time with space-can lead to a deeper and more nuanced understanding of Muslim cultures.Professor Mohammed Arkoun, the influential Arab intellectual, often called for audacious, free, and productive thinking about Islam and indeed Islamicate civilizations.He writes that our understandings of Islam as a religious phenomenon are woefully inadequate since we do not pay sufficient attention to a crucial element: "silent Islam."He defines "silent Islam" as "the Islam of true believers who attach more importance to the religious relationship with the absolute of God than to the vehement demonstrations of political movements."1Instead of focusing on this aspect, Professor Arkoun argues, scholarly discussions about Islam are monopolized by sociopolitical ideologies, such as Islamic revivalism.These, he claims, are in reality secular movements "disguised by religious discourse, rites, and collected behaviors."2Given Professor Arkoun's definition of "silent Islam," we may posit that these ideologies and their discourses of power, orthodoxy, and hege-
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.630 | 0.590 |
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".