The Norman Sicily Project: A Digital Portal to Sicily’s Norman Past
Bibliographic record
Abstract
The cultural heritage of medieval Sicily faces enormous challenges. Rich and diverse as it is, it is beset by numerous problems that have rendered it fragile and often inaccessible. The situation is such that many sites are unsigned. Others are very difficult to get to. And even others – ones that are more easily located – have erratic hours, essentially locking out the average visitor to Sicily unless s/he is willing to invest the time and have the language skills necessary to persuade residents in the surrounding area to get the access keys.Given these challenges, we are developing The Norman Sicily Project to document the cultural heritage of medieval Sicily during its Norman period (in other words, c. 1061–1194) so that a wide audience can learn about what was once there and what still remains. The site attempts to reconstruct what we know about the society by bringing together images, basic identifying information, geolocation data and, in some cases, videos, using modern web development techniques. It also offers genealogical information and visualization tools that can help visitors understand the data in new ways as well as sustainability data related to the monuments’ physical states. The intention is that the project will offer scholars, students and the general public who are interested in Norman Sicily the opportunity to learn from and collaborate with each other while suggesting a web-based model for other medieval communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".