Bibliographic record
Abstract
Introduction This chapter describes one university library's exploration and imple - mentation of mobile services and its subsequent contributions to broader, campus-wide mobile services initiatives. Ryerson University is a mid-sized urban university with a diverse student population of 23,000 undergraduate and 1,950 graduate students enrolled in five faculties: the Faculty of Arts; the Faculty of Communication and Design; the Faculty of Community Services; the Faculty of Engineering, Architecture and Science; and the Ted Rogers School of Management. Ryerson also has more than 65,000 annual registrations in its G. Raymond Chang School of Continuing Education, which is the largest in Canada. The university is primarily a commuter campus, with the majority of the students using public transportation. The library has a staff of over 85, including 27 librarians with a strong service focus. The collection consists of over 500,000 books, 2,500 print serials and an extensive electronic collection of over 35,000 journals, 90,000 e-books and a wide array of online databases. Environmental scan Prior to fall 2008, the only true mobile service offered by the library was a text-messaging service from the catalogue that enabled users to text the title, location and call number of an item to their mobile phones. This information could then be viewed when the user was in the stacks, looking for the book. The moderate successes of this service and the subjective impression that students were increasingly using smart phones encouraged the library to look more closely at the mobile environment as a venue for its services. To confirm our sense that smart phones were becoming more popular, particularly amongst the student population, we looked at several surveys conducted in 2008. The Pew Internet and American Life Project's March 2008 report, Mobile Access to Data and Information , indicated that within the 18- to 29-year-old demographic 85% were using text messaging and 31% were accessing the internet on their mobile devices (Horrigan, 2008). And in July 2008 a Nielsen report stated that 15.6% of US mobile phone subscribers used the mobile internet (Covey, 2008). Because neither of these reports commented specifically on the Canadian situation we had some concerns that the higher cost of data plans in Canada and the late introduction of the iPhone (in July 2008) might result in a different adoption rate in our environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 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".