„Petőfi név alatt valami régibb iró” = „Under Petőfi’s name some earlier writer…”
Bibliographic record
Abstract
Sándor Petőfi’s relation to earlier Hungarian literature was ambivalent. He found radical working with the past an unnecessary thing, an owl-like behaviour. However, he was keenly interested in the epic and material memories of the past (castles, ruins, etc.). Literature only partly belonged to this. The earlier authors could not be read in official, professional editions in his period; the scientific editions were published mostly in the second part of the nineteenth century. The anthology Handbuch der ungrischen Poesie (1826–1828), edited by Ferenc Schedel (Toldy), was the first breakthrough, whose approximately one quarter presents early Hungarian writers. On the other hand, a lot of seventeenth-eighteenthcentury texts spread on popular prints or in church songbooks. Some of them could be found in the original editions as well (Miklós Zrínyi, István Gyöngyösi, József Gvadányi). Popular poems in colleges conserved a few early texts, even some based on mediaeval compositions. Petőfi knew the rest of these, and their inspiration is reflected on his poems (rhymes, metrical forms etc.), but he avoided the closer imitation. The most important example is János vitéz (János, the valiant, 1844, publ. 1845), which, for the contemporary readers, could have seemed an imitation of earlier epic poems (Story of Árgirus, József Gvadányi’s works etc.).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".