Lukemista aukioloajoista välittämättä - e-kirjat osana yleisten kirjastojen kokoelmia
Bibliographic record
Abstract
In the new millennium, e-books have made their way to libraries. In Finland, they have been available in a few academic and special libraries since the 1990s, but their journey to public libraries has been slower. In many other western countries, such as Australia, Canada and United States, public library e-book collections have become more commonplace. This study offers a peek to these places by reviewing how e-books are acquired and made available to users in public libraries around the world. The study also examines how library staff and library users have received e-books, Finally, conclusions are drawn about the future prospects of e-books in public libraries. The research material consists of 26 key articles focusing on the role of e-books in public libraries. The articles were published within the period of 2001-2014 in international forums, both professional and scientific. The research material was analysed by means of qualitative review analysis combined with meta-synthesis. The results indicate that in the English-speaking world, around 90% of public libraries offer e-books by 2014, the average collection size being about 10 000 titles. The response to digital collections has been generally positive, if not as widespread as librarians have hoped. But headway is being made, and it looks like e-books are in public libraries to stay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 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".