Thesauri in the modern world: Research and prospects for application
Bibliographic record
Abstract
In today’s information society, where the amount of available information is constantly growing, the issues of semantic classification and data organization are becoming more and more relevant. Efficient information retrieval and analysis play a key role in scientific and applied fields, requiring innovative tools for semantic processing of texts and words. The study aims to analyse the structure, role, and potential of thesauri by means of statistical and structural analysis methods, as well as analytical-synthetic and comparative methods. The results emphasized the importance of thesauri in providing accurate and structured access to information in various fields. Statistical results showed that the broadest thesaurus categories in Library of Congress Subject Headings (LCSH) were art, library systems, medicine, culture, and media, followed by scientific research, linguistics, and semantics. The study presented a hierarchy between the subject area of research, thesaurus categories, narrowly focused terms, and ways to improve the classification and presentation of information. For example, the subject area art and culture included such thesaurus categories as sculpture, literature, painting, at the same time, the category sculpture can include such terms as sculpture group, statue, bust. Among the prospects of thesaurus development, we suggest improvement of information classification quality, efficiency of data analysis, optimization of catalogue search, development of new thesaurus structures, identification of interrelations between terms by means of semantic analysis, improvement of information accessibility of materials in libraries. The practical significance of the research lies in providing a basis for the development of effective strategies for thesaurus tools application in information technology, medicine, education, and art.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".