Bibliographic record
Abstract
A review of: Levine-Clark, Michael. “Electronic Book Usage: A Survey at the University of Denver.” Portal: Libraries and the Academy 6.3 (Jul. 2006): 285-99. Abstract Objective – To determine if university library users are aware of electronic books, and how and why electronic books are used. Design – Survey. Setting – University of Denver. Subjects – Two thousand sixty-seven graduate and undergraduate students, faculty, and staff. Methods – In Spring 2005, the University of Denver faculty, and graduate and undergraduate students were invited to participate in a survey about awareness and use of electronic books. A link to the survey was also posted on the library’s home page and on the university’s Web portal. The 19-question survey consisted of 11 questions to get feedback about electronic books in general, five questions focused on netLibrary, and the remaining were demographic questions. Eligibility to win one of two university bookstore gift certificates provided incentive to complete the survey. Main results – Surveys were completed by 2,067 respondents, including undergraduate students (30.1%), graduate students (39.1%), faculty (12.5%), and staff (11.8%). Results were reported by question, broken out by status (undergraduate students, graduate students, faculty) and/or by discipline (Business, Humanities, Nontraditional, Professional, Sciences, Social Sciences), and presented in tables or in the text. In general, most respondents (59.1%) were aware that the library provides access to electronic books. The library catalog and professors were the main ways respondents learned about electronic books. Approximately half (51.3%) indicated they had used an electronic book. Of those who indicated that they used electronic books (1,061 respondents), most (72%) had used electronic books more than once. The main reasons mentioned for choosing to use an electronic book included: no print version available, working from home makes getting to the library difficult, and searching text in an electronic book is easier. When asked about typical use of electronic books, most respondents indicated they read only a part of an electronic book; only 7.1% of 1,148 respondents indicated they read the entire electronic book. In answer to a question about choosing the print or electronic version of the same book, 60.7% responded that they would always or usually use print, and 21.5% indicated they would always or usually use electronic. The amount of material to read, the need to refer to the material at a later time, and the desire to annotate or highlight text are all factors that influence whether users read electronic books on a computer or PDA, or print out the material. U.S. government publications and netLibrary were the electronic resources used the most by survey participants. Conclusion – The results of this survey suggest the need to market availability of the library’s electronic books. Problems associated with the use of electronic books are related to reading large amounts of text on a computer screen, but a reported benefit is that searching text in an electronic book is easier. Responses to the survey suggest that the use of electronic resources may not be generic, but rather depends on the type of resource (content) being used. The author notes that this finding should lead to further investigation of which items will be preferred and used in which 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.098 | 0.022 |
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".