Breaking the Spell of the Baron de Tott: Reframing the Question of Military Reform in the Ottoman Empire, 1760–1830
Bibliographic record
Abstract
of the persistent and unanswered questions in the 'West against the Rest' debate embedded in recent world histories asks why the rest, including the Ottomans, did not keep up with Europe.1 Why did the Ottomans not adopt Western technology, for which one is to read weapons, or clocks, when their own military systems began to fail them so badly after 1700? However unsatisfactory, there are now several alternatives to the stereotypes of religious obscurantism, conservatism, and backwardness most often proffered as the primary cause of the Ottomans' ineptitude.2 Assumptions about the role of culture, in particular religion, in the military context, and the sources we choose as evidence when analysing the reorganization of society, often prove inadequate to explain Ottoman history. This article on the influence of sources and the debate on the relationship between reform and technology argues for multiple causality, and privileges the role of technical exchanges, or 'conversations',3 over the hierarchy of knowledge and power embedded in Enlightenment debates about Ottoman civilization which persist to the present. The period under consideration, 1760-1830, covers the transformation of the Ottoman ancien regime during the reigns of Mustafa III (1757-74), Abdulhamit I (1774-89), Selim HI (1789-1807), and Mahmud II (1808-39), the last considered to be the architect of nineteenth-century Ottoman
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".