Bibliographic record
Abstract
Tišnov, in the northern Brno region, became an important municipal community back in the 13th century. The oldest surviving municipal book dates back to 1550, although it many places it refers to an older municipal book, now lost or destroyed. We know the incipit to the lost book from a report on the history of the town dating from 1728, together with a list of the nobles who, according to this lost book, had bought houses or property in Tišnov. The initial entry mentions the establishment of the book on 24th September 1454 by the members of the municipal administration: these were the town treasurer, the vogt, a college of twelve councillors headed by the burgomaster, and the town scribe. The book was evidently established as part of the changes that occurred in the town after the Hussite Wars, when the original gentry, the Porta coeli Cistercian monastery, were once again granted certain powers in Tišnov. \nHowever, the decisive influence continued to be exerted by the Lords of Pernštejn, who took possession of the entire estate in around 1428, and controlled affairs in the town through the newly-established office of the town treasurer. Entries were then continuously added to the book until the mid-16th century, and the names of the nobles listed there belong particularly to the second quarter of the 16th century: these are the owners of the surrounding estates, rentiers from the lower nobility and clients and members of the administrative apparatus of the Pernštejn domain.\n
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".