Bibliographic record
Abstract
Purpose The purpose of this paper is to assess how Canadian academic libraries have responded to the rapidly evolving mobile environment and to identify gaps in the services provided, while suggesting areas for future development. Design/methodology/approach The paper conducted an examination of the mobile content and services provided by the libraries of the member institutions of the Association of Universities and Colleges of Canada (AUCC). Based on this examination, the paper describes the current state of mobile librarianship in Canadian academic libraries. A review of the literature places the investigation in its broader context. Findings Only 14 percent of AUCC libraries currently advertise some type of mobile web presence, with mobile web sites being prevalent over downloadable apps. Examples of content and services are highlighted to illustrate current trends and to provide insight into future directions for developing mobile services. Practical implications This study raises awareness of the importance of mobile technology for academic libraries and the need to address the lack of mobile content and services provided by most Canadian post‐secondary institutions. The paper also identifies best practices exhibited by the surveyed libraries. Originality/value This is the first exploration of this type into how academic libraries in Canada have responded to the mobile environment. The value of this research is in helping libraries identify and address shortcomings in the mobile content and services they provide, and in highlighting efforts by libraries to address their users' needs in this area.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".