Erasing oral residue and correcting scribal error: re-interpreting the presence of mnemo-technical practices in Ottoman manuscripts in the early modern period
Bibliographic record
Abstract
The international conference Culture of Memory in East Central Europe in the Late Middle Ages (1000-1600) was held at Poznań University Conference Centre in Ciążeń (March 12-14, 2008). The conference was attended by participants from Canada, the Czech Republic, France, Germany, Hungary, Macedonia, Norway, the UK, the USA and Poland. The event was organized by Poznań University Library (represented by Rafał Wójcik) with the co-participation of the Centre of Theoretical Studies at Charles University in Prague (represented by Lucie Doležalová) and Eötvös Loránd University in Budapest (Gábor Farkas Kiss). The conference was partly financed by Poznań University Library and partly from a grant awarded by the International Visegrad Fund. \n \nMs Norton’s presentation entitled Re-interpreting the presence of mnemo-technical practices in Ottoman manuscripts in the early modern period dealt with the production, transmission and reception of texts in early modern Ottoman manuscripts. These processes were shown as a slow change in the transition from oral culture to literate culture. Ms Norton’s paper was an interesting contribution and reference to work of such researchers in orality as Walter J. Ong or Michael T. Clanchy. Ms Norton also pointed out that the content of the corpus of 25 manuscripts known as Gazavat-i Tiryaki Hasan Paşa had been known to the inhabitants of the borderland between the Ottoman Empire and the Habsburg Monarchy in South-East Europe of the time.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".