Bibliographic record
Abstract
Purpose The purpose of this paper is to determine how students at the University of Regina would like to interact with the library on their mobile devices and how to best construct a mobile site to suit the university community's needs. Design/methodology/approach A survey was designed to gather feedback from the university community on their use of mobile devices and how they would want to use the library's resources and services via their mobile device. This survey also attempts to better understand how academic libraries can provide effective mobile services. A questionnaire was developed and distributed to several Canadian academic libraries. Its purpose was to discover what other institutions were doing with mobile technologies. Findings The survey found that 95.4 percent of students that responded to the survey had a smartphone and 75 percent of them used their mobile phone to access the web. The survey indicated that the library catalogue was the most popular resource chosen to become mobile enabled. The questionnaire distributed to other Canadian academic libraries showed that some libraries were designing and building web apps, while others were creating native apps. Originality/value With the increase of mobile technology availability and the demand for accessible mobile content, it is imperative that libraries examine how they can provide services to their patrons within this medium in order to continue to provide valuable services. Mobile technologies are constantly changing, so continuous assessment in this area is of importance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".