Faculty in the Applied and Pure Sciences May Have Limited Experience with E-books
Bibliographic record
Abstract
A Review of: Bierman, J., Ortega, L., & Rupp-Serrano, K. (2010). E-book usage in pure and applied sciences. Science & technology libraries, 29(1-2), 69-91. https://doi.org/10.1080/01942620903579393 Abstract Objective – To determine the usage of and attitudes toward e-books among faculty in the applied and pure sciences. Design – Online survey and in-person interviews. Setting – A large public university in the United States. Subjects – 11 faculty members. Methods – Participants completed an 11-item survey covering demographic data and questions about electronic book experience and preferences. This was followed up by an in-person interview with the researchers. The interviews were structured into three sections: opening questions about e-book usage, an interactive demonstration and discussion of two preselected e-books, and final follow-up questions. Interviews followed a general script of prepared questions, but also encouraged open discussion and dialogue. Main Results – Most participants in the study reported limited experience with e-books and only 3 of the 11 participants reported using library-purchased e-books in their research and instruction. Participants noted ease of access and searchability as key advantages of e-books. Concerns included the belief that reading and learning is more difficult on a desktop computer, as well as concerns about the stability and reliability of e-book access. Participants also felt negatively about the necessity to create a new login profile and password to access e-books. The study found no difference in the way faculty in pure and applied sciences approached e-books. Conclusion – The authors determine that e-books will likely become more commonly used in academia. Users want e-books that are easy to use and customizable. In addition, the authors conclude that librarians need to understand their patrons’ needs as e-book users and proactively promote and market their e-book collections.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 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".