Usability evaluation of Ebrary and OverDrive e-book onlinesystems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The publishing industry's largest growing sector is e-book sales (Macworld, 2004). For example, e-book sales for the first quarter of 2004 in the USA were up 46%, and e-book revenues were up 28% compared to the same quarter in 2003, according to the Open e-book Forum (2004). One sector likely to benefit from this growth is higher education, because the provision of e-books can be seen as a core feature of integrated e-learning strategies and synergies such as managed learning environments (MLEs) and virtual learning environments (JISC, 2003). Although many problems surround the provision of e-books, such as pricing and licensing ambiguity, budget constraints, and content bias towards the American market (Armstrong et al., 2002), e-books are making inroads into academia. Ebooks are being purchased from individual publishers, and aggregators of e-books, such as ebrary, OverDrive and NetLibrary, already established in the USA, are starting to penetrate the UK market. The latter provide integrated solutions for libraries based on remote-access servers that accumulate collections of e-books provided by different publishers. Although some research has been undertaken investigating the use of aggregators in public libraries (Dearnley et al., 2005) little has been done in the academic sector, particularly with respect to the usability of such services.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it