[The biomedical periodicals of Hungarian editions--historical overview].
Bibliographic record
Abstract
INTRODUCTION: The majority of Hungarian scientific results are published in international periodicals in foreign languages. Yet the publications in Hungarian scientific periodicals also should not be ignored. AIM: This study analyses biomedical periodicals of Hungarian edition from different points of view. METHODS: Based on different databases a list of titles consisting of 119 items resulted, which contains both the core and the peripheral journals of the biomedical field. These periodicals were analysed empirically, one by one: checking out the titles. RESULTS: 13 of the titles are ceased, among the rest 106 Hungarian scientific journals 10 are published in English language. From the remaining majority of Hungarian language and publishing only a few show up in international databases. Although quarter of the Hungarian biomedical journals meet the requirements, which means they could be represented in international databases, these periodicals are not indexed. 42 biomedical periodicals are available online. Although quarter of these journals come with restricted access. 2/3 of the Hungarian biomedical journals have detailed instructions to authors. These instructions inform the publishing doctors and researchers of the requirements of a biomedical periodical. CONCLUSIONS: The increasing number of Hungarian biomedical journals published is welcome news. But it would be important for quality publications which are cited a lot to appear in the Hungarian journals. The more publications are cited, the more journals and authors gain in prestige on home and international level.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.055 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".