Peculiarities of Europeana Metadata as a Set of Information for Describing Digital Cultural Heritage
Bibliographic record
Abstract
The relevance of the research. European integration is the main and unchanging foreign policy priority of Ukraine. Further development and deepening of relations between Ukraine and the EU are the main direction of its development. A new stage in the development of EU-Ukraine treaty relations, which began with Ukraine’s accession to the EU candidate status in June 2022, requires further raising awareness of Ukraine’s European integration among all members of society. Libraries can contribute to the formation of objective motivations among the population by understanding the practical benefits of cooperation with the EU. One of the ways of such integration for Ukrainian libraries is to become part of the European library network.
 The purpose of research is to identify the features of Europeana metadata for further digitization of library collections in Ukraine in order to preserve cultural heritage.
 The methodology. To achieve this goal, theoretical and general scientific research methods, such as analysis, synthesis, induction, and deduction, were used. The data visualization method and the method of generalization were used to present the Europeana data models.
 The results. The article defines the types of the Europeana operational model, provides a general overview of the classes defined in the Europeana data model and the process of interaction between Europeana data and wikidata.
 The scientific topicality. The article summarizes previous, primarily foreign, research on the peculiarities of the development of the Europeana funds. The article presents the data models of the Europeana that function to fill the web portal.
 The practical significance. The results of the research are presented in the form of Europeana data models and the definition of Europeana metadata types (descriptive, structural, administrative, markup language).
 The conclusion. The current operating model of Europeana uses the Open Archives Initiative’s Metadata Collection Protocol (OAI-PMH) and the Europeana Data Model (EDM) to import data through Metis, Europeana’s data collection and aggregation service. However, OAI-PMH is an outdated technology that is not web-oriented, resulting in high maintenance costs, especially for smaller institutions. Metadata can be divided into several categories, or types, for different data management purposes. Traditional library cataloging focuses, for example, on the identification and description of resources, but there are obviously other types of metadata that carry valuable information (structural, administrative, markup language).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".