#Hashtag Social Media Experiences: Building Positive Patron–Library Relationships
Bibliographic record
Abstract
The ubiquitous presence of social media has changed the way libraries communicate with their patrons. Academic libraries usually employ social media to provide instant feedback to queries and promote library services to students and faculty. However, using these tools solely for such purposes means not fully utilizing their potential. Properly applied, social media can help build and maintain positive relationships between libraries and communities they serve. Social media experts (e.g.: Quesenberry, 2021) argue that in order to build these relationships, an organization should take a “bottom up” approach to social media by engaging with patrons through social media listening and monitoring, content creation, community management, and engagement. Using this approach, the study examines how five academic libraries in Atlantic Canada employ social media to build long-term positive relationships with students and their parents, faculty, and staff. It also examines how electronic word-of-mouth (eWOM) influences these relationships, since social media can either engage or alienate patrons. The authors also devise a set of recommendations and discuss best practices of social media use by academic libraries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".