Bibliographic record
Abstract
Purpose The purpose of this paper is to review one library's experiences of creating mobile services and illustrate how, by developing expertise in emerging technologies, libraries can foster partnerships with other groups on campus and play a leading role in providing relevant student‐centred services. Design/methodology/approach The paper begins with a brief summary of mobile services offered by the Ryerson Library prior to the fall of 2008, discusses the results of a mobile device survey conducted that semester, and outlines the resulting mobile services that were developed by the Library which led to a campus‐wide collaboration to develop the framework for a student‐led mobile initiative. The technical framework and project management issues are also discussed. Findings A survey performed by the Ryerson University Library in the fall of 2008 indicated that smart phones were owned by approximately 20 percent of the student population but that within the next three years this figure could reach as much as 80 percent. To remain relevant, it is important that libraries adapt their services to this new environment. Practical implications The paper illustrates how library services can be adapted to the mobile environment and how the library can play a role in broader campus mobile initiatives. Originality/value All libraries will be interested in exploring the library services that were developed and adapted for mobile devices and of particular interest to academic libraries will be the building of collaborative relationships with other academic departments to provide services to students.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".