On eBooks in academic libraries: an article based on a presentation at the Library 2.014 Conference
Bibliographic record
Abstract
Purpose – The purpose of this paper is to demonstrate and discuss some of the commonly held misconceptions about using and managing eBooks in academic research libraries and to outline what skills, abilities and knowledge are required of librarians and other library staff who work with eBooks. eBooks are an important presence in the vast majority of academic libraries. It is reasonable to expect that this presence will increase in the years to come. The value of eBooks to many students, faculty and researchers is undeniable and their availability has created new learning and teaching opportunities which were not possible with print-only library collection. Design/methodology/approach – eBooks bring multiple benefits to the academic environment. Findings – eBooks provide portable information resources for students and researchers doing fieldwork. eBooks have the potential for relieving pressure on space in some libraries. Some eBook platforms offer students new and enhanced ways for interacting with library materials, including accessibility features. Originality/value – The addition of eBooks to academic library collections addresses a growing preference among some patrons for information in electronic format.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 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".