Unfolding Ottoman architecture in writing: theory, poetics, and ethics in Cafer Efendi's "Book on Architecture"
Bibliographic record
Abstract
This dissertation is the first comprehensive study to examine Ottoman architecture's intrinsic link with various modes of knowledge in the early seventeenth century, based on a critical interpretation of Cafer Efendi's book on architecture. It aims to disclose architecture's inherent bond to other arts and sciences from the perspective of an Ottoman scholar, who was a close friend of the Ottoman chief architect, Mehmed Agha. My intention is to understand architectural ideas as they intersected with various intellectual and cultural mentalities and practices predominant in the late sixteenth and early seventeenth centuries. I argue that Cafer's book discloses how architecture was deeply embedded within the theoretical, ethical, and poetic perceptions of his milieu. The dissertation further argues that Cafer's dependence on various literary forms, such as poems, stories, dictionaries, as well as mathematical sciences, in order to impart architectural knowledge, sheds significant light on his perception of architecture as an art and science conducive to both sensuous and spiritual experiences, in addition to intellectual inquiries. Thus, my thorough examination reveals the mediating role of architecture as a cultural phonemenon within the broader cultural and intellectual context of the early modern period. The union of the poetic, theoretical, and ethical dimensions of architecture elevates architecture's status, for an Ottoman scholar like Cafer Efendi. Concurrently, the position of the chief architect Mehmed Agha is promoted to a higher level due to his intellectual and practical skills, as much as his ethical virtues, which were significant as a means to participate in the preservation of order, understood both as social and cosmic harmony.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".