Bibliographic record
Abstract
Contemporary Shevchenko studies have various nuances, which are increasingly updated during the celebration of Kobzar's birthday. In today's conditions, Google offers the Google Trends search tool for use, which makes it possible to assess the level of interest or popularity of a certain topic, which can be of great importance for the development of international cultural programs. The article proposes the use of an innovative approach to the assessment (measurement) of interest in the topic of Shevchenko studies by means of Google in Ukraine and the world. The interest (demand) on the subject of Shevchenko studies in Ukraine and the world was assessed using Google Trends techniques in modern conditions, it was determined how often Internet users turn to the figure of Taras Shevchenko in the world, how the topic of Shevchenko studies is widespread in the cultural information field of various countries (using the example of Poland), which regions of Ukraine and the world are most often interested in the figure of Taras Shevchenko nowadays. Interest in the name of Taras Shevchenko in the English language over the last year in the world has increased sharply in the first week of the beginning of the aggression against Ukraine, which confirms the association of Ukraine with the name of Taras Shevchenko in the world. Regarding the country distribution of interest, it is statistically significant, there are more than 30 countries in the list, their composition has changed since 2004 in the direction of European countries and Canada. It has been established that the topic of the figure of Taras Shevchenko himself is relevant throughout the modern world, but the topic of Shevchenko studies needs further informational support from the citizens of Ukraine and their friends in all countries of the world.
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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 | 0.023 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.052 | 0.051 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".