Bibliographic record
Abstract
When royal power started weakening in Hungary in the last third of the thirteenth century, the Hungarian royal authority in the Dalmatian towns also started to lose influence, and by the first third of the thirteenth century, most of the towns previously under Hungarian rule had become Venetian territories. The reoccupation of these towns and even more lands on the Eastern Adriatic coast could be connected to King Louis I of Hungary, who defeated Venice in 1358 in the war between Hungary and the Italian city state. This study focuses on the king’s exercise of power in Dalmatia, particularly the economic aspects of royal policy and the place of Zadar in this policy. My analysis also focuses on the formation of a Hungarian center in Dalmatia from the twelfth century and on how King Louis turned away from the policies of the previous kings of Hungary. My intention is to highlight the economic importance of Zadar, the process of the formation of an economic and trade center of Hungary, and also the formation of the Dalmatian elite, with a particular focus on the citizens of Zadar, who were in the closest circles of the Hungarian king. The focus will be also on the integration of the coastal territories into the mainland of Hungary under the reign of King Louis I.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".