Bibliographic record
Abstract
Some considered 2000 the year of the e-book, and due tothe dot-com bust, that could have been the format’s highwater mark. However, the first quarter of 2004 saw thegreatest number of e-book purchases ever with more than$3 million in sales. A 2002 consumer survey found that67 percent of respondents wanted to read e-books; 62 percent wanted access to e-books through a library.Unfortunately, the large amount of information writtenon e-books has begun to develop myths around their use,functionality, and cost. The author suggests that thesemyths may interfere with the role of libraries in helpingto determine the future of the medium and access to it.Rather than fixate on the pros and cons of current versions of e-book technology, it is important for librarians tostay engaged and help clarify the role of digital documents in the modern library.
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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.046 |
| Scholarly communication | 0.017 | 0.036 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".