Bibliographic record
Abstract
The chapters in this book present recent research and information on the use of mobile technology in libraries. The authors of these chapters are pioneers in their respective countries and are contributing to the advancement of the use of mobile technology to transform libraries. They are laying the groundwork for the libraries of the future in the mobile revolution. As the educational system and society change, libraries need to change too, so as to continue to provide quality service to customers. The libraries of the future will not be the same as we experience them today. They will be transformed to provide access to 21st-century services. The transformation of libraries is needed for the following reasons: 1 The new generation that is entering the education system has different expectations from previous generations. In addition to the present generation, libraries will also have to cater for future generations. 2 Technology is changing rapidly, becoming smaller, more powerful, more virtual and more user friendly. A recent news article about changes in the smartphone market had the title ‘Don't Blink: you'll miss it’ (Canadian National Post, 2011). This describes very well the rapid changes in technology. 3 The internet is becoming faster as we move to Internet 2 and Internet 3. This will provide fast connectivity to people, especially those in remote locations. 4 The amount of information available for access is growing fast. According to Eric Schmidt, ‘every two days we create as much information as we did from the dawn of civilization up until 2003’ (Schmidt, 2010). This includes user-generated information using social software. 5 The education model is changing, moving away from group-based instruction and towards individualized instruction. There are initiatives around the world to establish online and virtual educational institutions, and existing group-based institutions are moving to blended learning that includes both online and face-toface instruction. 6 There is more emphasis on lifelong learning, since people will change careers frequently, and on the increasing availability of information. There are also initiatives around the world, such as the Millennium Development Goals, to provide education for all and to improve people's quality of life. 7 Learning for just-in-time application is taking the place of learning ‘just in case’ the information will be needed. Rather than sending employees to take an entire course, organizations are using e-learning and mobile learning to provide just-in-time training.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.144 |
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".