Leveraging apps for research and learning: a survey of Canadian academic libraries
Bibliographic record
Abstract
Purpose – The purpose of this paper is to assess the response of Canadian academic libraries to the rapid proliferation of mobile application (apps), many of which are useful for research, teaching, and learning. Design/methodology/approach – A survey was conducted to identify existing initiatives that address the use of mobile apps to facilitate research, teaching, and learning at the libraries of the 97 member institutions of the Association of Universities and Colleges of Canada (AUCC). Based on this survey, this paper describes how apps are promoted, curated, organized, and described by today’s academic libraries. A review of the literature places this survey in its broader context. Findings – In total, 37 per cent of AUCC member libraries include links to mobile apps in their web site. Larger, research-intensive universities, tend to leverage apps more frequently than smaller institutions. Examples of how academic libraries are promoting apps provide insight into how academic librarians are responding to the proliferation of mobile technology. Practical implications – The results of this survey highlight trends with regard to this emerging service opportunity, help to establish current best practices in the response of academic libraries to the emergence of mobile apps, and identify areas for potential future development. Originality/value – This is the first study of its kind to explore and describe how third-party apps are used and promoted within an academic library context.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".