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
This special IRRODL issue on mobile learning is timely because of the proliferation of mobile technology in society, globalization, and the need to re-examine how learning materials are designed and delivered for the new generation of learners. In today's world, people are on the move and are demanding access to learning materials and information anytime and anywhere. At the same time, there is increasing use of mobile technology in different sectors of society to meet the needs of people on the move. In business, there is increasing use of mobile technologies for individuals to conduct their business anywhere and anytime. In healthcare, medical staff are using mobile technologies to access just-in-time information and to enter information in real time. People working in the field away from the central office use mobile technologies to access information and to communicate with other workers. Also, younger generations of learners are using mobile technologies for entertainment and socialization. These learners are using mobile devices to access information and multimedia materials and to communicate with friends. These new generations of learners do not see technology as something foreign. They readily accept technology and consider technology to be part of their lives. Moreover, the use of mobile technology is a 21st Century skill that students and workers must have to function in society.
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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.000 | 0.006 |
| 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