Bibliographic record
Abstract
Introduction Library marketing expert Nancy Dowd published an article in 2013 titled ‘Social Media: libraries are posting, but is anyone listening?’ (Dowd, 2013). Dowd points out that although the majority of libraries use social media to disseminate information to their communities, many do not keep track of their efforts or claim success in getting followers to interact. With this in mind, libraries that want to create or revitalize their social media presence should consider devising a plan. This chapter presents highlights from a social media case study carried out at the Emily Carr University (ECU) of Art and Design Library in Vancouver, British Columbia. Through the experience of the study, the authors outline ways in which organizations can develop objectives for their social media usage, strategies to increase online presence and interaction with their communities and methods to assess their presence. They also discuss some innovative ways libraries use social media, focusing on the visually rich field of art libraries. Emily Carr University of Art and Design Library: social media case study In spring 2013, the ECU Library made a push to energize and bolster its social media presence and formed a social media committee to support this project (Webb and Laing, 2015, 137–51). The committee met to discuss why social media was being used, what goals would be achieved by its use, who would be responsible for each platform, the type of content that would be posted, and how success would be measured. These were some of the goals set out by the committee: • Raise the online profile of the Library by increasing the number of followers. • Inform users of special events and programming. • Help define the Library's role within the broader university community. • Gain overall support for the Library from the community in general. A social media policy was drafted to reflect the needs of the Library and to provide concise guidelines for staff to follow when representing the Library on its social media platforms. Each committee member took responsibility for a social media platform, posting content and keeping track of metrics and anecdotes of usage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".