Apps for academic success: Developing digital literacy and awareness to increase usage
Bibliographic record
Abstract
As a consequence of the high adoption levels of mobile technology, users are increasingly accessing academic library-subscribed content via vendor-supplied mobile applications (apps) or responsive websites. However, users may be unaware of the existence of some standalone apps and might miss benefi tting from available apps at their most significant point of need. This paper outlines the McGill Library’s multifaceted approach to promotion and outreach to increase awareness and usage of mobile apps in an effort to provide additional access points for the library’s e-resources. A variety of online and traditional promotional methods were employed, such as faculty news e-bulletins, an app web-guide, images on the Library home page slideshow, and in-person demonstrations, to advertise two of the Library’s subscribed apps, PressReader and BrowZine. Complementing this approach, four different workshops were offered at different times during an academic year targeted to specific audiences: faculty, university communications and library staff, and students. The authors describe the content and results of these initiatives showing how specific promotional strategies appear to have a greater impact on usage. They conclude with thoughts on how current behaviours in mobile usage might begin to affect the future direction of mobile access to library-subscribed e-resources.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".