Tablet adoption and implementation in academic libraries: a qualitative analysis of librarians' discourse on blogging platforms
Bibliographic record
Abstract
Purpose The purpose of this paper is primarily to report on a 2011 online discussion on tablets and their adoption in libraries, as observed by the researcher in blog postings and micro‐blog postings. Design/methodology/approach The researcher examined blogs and tweets about the diffusion of tablets in academic libraries to find out why early adopters or academic librarians adopted tablets and implemented them into library services. Findings Results reveal that academic librarians and libraries adopt and integrate tablets into library services because they can offer wireless access to the library's e‐collection and e‐resources in ways better than e‐readers or smartphones and because librarians have some level of familiarity with using tablets for their own work purposes before they considered extending such purposes to users. Practical implications Academic libraries are investing in devices to facilitate users' access to growing e‐resources. Tablet devices are one such option. However, many tablets are expensive, equalling or totalling more than the costs of laptops. The decision to adopt and implement them into library services needs to be informed by the experiences of others, in order to determine if it is a worthwhile purchase. Originality/value This paper departs from the general pattern of library literature on the subject of tablet adoption, by breaking with the tradition of being only informed by practice and emerging trial and error, to a more reflective approach to those experiences informed by Rogers' theory of the diffusions of innovations.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".