Bibliographic record
Abstract
Chris Hann and the „Property Relations“ Group: The Postsocialist Agrarian Question. \nProperty relations and the rural condition. Halle Studies in Anthropology of Eurasia 1. \nMünster: LIT-Verlag 2003, 472 Seiten. \n„... szolgálatra ítéltél ...“ Bálint Sándor emlékkönyv [… Du hast zum Dienst verurteilt … \nSándor-Bálint-Festschrift]. Hrsg. von Gábor BARNA, unter Mitwirkung von Ferenc \nSZABÓ und Magdolna SZABÓ. Szeged: LAZI Könyvkiadó 2004, 404 Seiten. \nFerenc POZSONY: The Hungarian Csango of Moldova. Buffalo–Toronto: Corvinus Publishing \n2006, 292 Seiten. \nMelinda ÉGETŐ (Hrsg.): Szőlőhegyek történetének forrásai I–IV [Geschichtsquellen der \nWeinberge I–IV]. Budapest: L’Harmattan Könyvkiadó 2001, 246 Seiten, 2002, 335 Seiten, \n2004, 260 Seiten, 2006, 267 Seiten \nVilmos TÁNCZOS: Folklórszimbólumok [Folkloresymbole]. Néprajzi egyetemi jegyzetek \n2. Kolozsvár: KJNT–BBTE, Magyar Néprajz és Antropológia Tanszék 2006, 344 Seiten.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.459 | 0.366 |
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".