Bibliographic record
Abstract
As mobile devices become more prevalent in society, educational and other organizations have started to make their libraries more mobile-friendly to provide flexible service to learners, educators, researchers and the public. As the subtitle of this book, ‘From devices to people’, suggests, the needs and characteristics of users should be kept in mind when designing services for the user. As the use of mobile technologies in libraries increases, the devices and the user interface have to be user-friendly to facilitate seamless access. This can only be done by conducting research on the use of mobile devices in libraries. The chapters in this book are written by librarians from around the world who are experts in their field and who are leaders in the use of mobile technologies in libraries. The chapters present information and best practices that can be used to provide mobile-friendly service to users. The shift for providing mobile library services to users is being driven by many forces. The users today, especially the young generations of users, are comfortable using mobile devices and are using the devices for everyday activities and for socialization. As a result, they will expect to access library materials and services using the mobile devices. There is a shift to digitization around the world, in which learning materials and information are being digitized for access by electronic devices. Textbooks and journals are being digitized for delivery on a variety of technologies, including mobile technologies. The internet is becoming more mobile-friendly and organizations are making their websites mobile-friendly. Big data is being created, which makes interpretation of the data more complex. Massive Open Online Courses (MOOCs) are being delivered to a large number of learners who are from different backgrounds, cultures, locations, academic levels and disciplines. Most importantly, there is an information explosion because of the increasing use of social media, digitization and user-generated information. The information explosion is a challenge to users, since they have to filter a large amount of information to get the correct information they need. Librarians of the future must take the above trends into consideration when developing mobile-friendly services for users who are mobile.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.237 | 0.114 |
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".