Professional work and learning with smartphones: a comparative study of school librarians from Australia, Hong Kong and United Kingdom
Bibliographic record
Abstract
The study explores the role of mobile technologies in school librarians‟ daily work and professional learning and development. The aim of the study is to examine how school librarians search, access, use and share information via their smartphones, what smartphone functionalities they use for these purposes, and what apps and social media are most frequently used. The study is based on empirical research conducted on a sample of school librarians/teacher librarians in Australia, Hong Kong and United Kingdom. Data were collected with online survey. The major finding of this study is that school librarians in Australia and Hong Kong widely use smartphone technologies for their daily needs and for professional work and learning. Various smartphone functionalities tools are used to satisfy these varied needs. Also school librarians from Hong Kong use various smartphone functionalities more frequently than study participants from Australia and UK. Barriers to smartphone use are also discussed but it seems that they do not detract school librarians from using smartphone technologies for their daily professional activities.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".