Tanult orvosok a középkori Magyar Királyságban
Bibliographic record
Abstract
learned Medical doctors of the Medieval hungarian KingdoM. he present paper aims at collecting the particulars of medical doctors of the medieval Hungarian kingdom, primarily the ones belonging to persons who attended a university. he research had to reckon with the fact that the doctors were referred to in the respective sources by several Latin names (doctor in medicine, medicus, physicus, barbitonsor, etc.) but not all of these refer to a person who attended a university. We accept only the person as a learned doctor whose university attendance can be documented either by his presence in the matricula of a university or by his degree mentioned in a source. Another attendant problem was the deinition of Hungarian, since, for example, most doctors practising in the royal court came from abroad but owing to their service they often gained Hungarian citizenship or, moreover, nobility. After examinig these questions we managed to collect 69 persons who have evidence of their studies or graduation from 1226 till 1525, mainly from the second part of the 15th century or the irst quarter of the 16th century. heir prosopographical data can be found in the Database at the end of the paper. Most of the students studied medicine in Vienna (22 persons) or at an Italian university (31 persons) and almost half of them gained a degree (35 persons). In accordance with the present phase of the research most doctors had an ecclesiastical career, mainly as a canon (12 persons), however, a few of them practised as municipal physicians (15 persons).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".