Bibliographic record
Abstract
Purpose Academic librarians who are planning for the future need to be knowledgeable about the short‐ and long‐range outlook for print. They must also consider what will happen if libraries abolish most or all of their books. This paper aims to explore current and future academic e‐book usage, and to suggest ideas for response to collection changes. Design/methodology/approach This article examines a wide range of studies and comments on this timely topic. Findings The disparity between the reception of e‐books in the general population and the adoption of them in the academic world suggests that print is still important to faculty and students. Given the advances in e‐book technology, the increasing popularity of online/distance education courses, the adoption of the new EPUB 3 format, and the ubiquity of mobile devices, e‐books are expected increasingly to replace print volumes in academic libraries. Originality/value What has received little attention in the literature is the complexity of the issue of e‐book reception in the academic world. This article looks at current and future e‐book usage from the perspective of several large studies on diverse aspects of academic life, including students' perceptions of libraries, their information‐seeking behaviors, faculty research habits and information needs, students' reading habits, and the impact of emerging technologies on teaching and learning. Providing insight into current and future academic e‐book trends, this article suggests practical ways to respond to these trends.
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".