Tough Love: Guiding Student Researchers Toward a Better Future for E‐Books
Bibliographic record
Abstract
EPUB has emerged as the standard format for e‐books due to its numerous advantages over PDF, including superior accessibility, enhanced navigation, lighter file sizes, optimization for mobile devices, and support for non‐English languages, to name a few. However, there is little understanding of EPUB’s advantages among end users and little appreciation for EPUB’s potential in academic libraries. This paper provides a literature review and perspectives from a publisher, an aggregator, and end users (higher education library) about solutions that drive increased knowledge and use of the EPUB format for e‐books in the academic library. It will summarize the reasons for EPUB’s ascendance among academic publishers, explain how PDF e‐books present barriers to innovation and real problems for accessibility, and argue that the scarcity of EPUB in the academic library is mostly due to a lack of awareness of its benefits. It will give librarians some ideas on how to begin integrating EPUB into research instruction, and ultimately, it will suggest that both librarians and vendors take an active role in shaping the habits and thus demands of their users.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.048 | 0.038 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".