Bibliographic record
Abstract
In recent decades, the scope of library activities has changed. In addition to providing traditional resources and services, today libraries themselves are becoming developers of digital content and providers of access to electronic content. The user base of libraries is also undergoing significant changes: there are changing the user generations, employment trends, areas of interest and habits. Realizing that marketing activities can increase user loyalty, form public opinion about both — a particular institution and libraries in general, increase the visibility of library resources and enlarge market share, the most active specialists are adapting marketing to the conditions of libraries operation. In 2019, there was organized the study on the websites of libraries in Russia, Australia, the United States and Canada, Western and Eastern Europe, Latin America, as well as in a number of countries in South and South-East Asia in order to identify the main trends in the organization of library promotion in the Internet environment. There were considered the websites of public, national, University and academic libraries. Library websites were analysed for the use of 10 most common tools in Internet marketing, such as: the transition to a modern web site design, effective linking with social networks, marketing in social networks (Social Media Marketing, SMM), the use of banner advertising, the availability of subscription to e-mailing, the publication of press and post releases, the presence of a blog in the library domain, the availability of content evaluation and sharing tools in social networks. The study conclusion was the identification of trends in the development of library sites over a large area and the compilation of heat maps, which clearly demonstrate such trends, where the libraries of the countries of North America, Australia and Northern Europe showed greater consistency with the parameters selected for the study; Central and Southern Europe, Russia and part of the countries of Latin America are in the transition zone, and the least compliance are found in the countries of South Asia. The most common means of promotion is the publication of press and post releases. Among the least used marketing technologies are blogs in the library domain, embedded video and Internet broadcasts, as well as work on optimizing sites for an audience from social networks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".