Pinterest as a Tool: Applications in Academic Libraries and Higher Education
Bibliographic record
Abstract
Pinterest, a pinboard-style social photo-sharing website, has become a popular site for many individuals who collect images that help them plan, organize, and explore any topic of interest. Launched in March 2010, Pinterest now has over 10 million users and is continuing to grow. Libraries and educators are starting to explore this new type of social media and how it can be used to connect with and inspire their patrons and students. This article will look at how the University of Regina Library is currently using Pinterest to engage and interact with the University community. This social tool has appealed not only to librarians but educators as well. Pinterest is starting to have an impact on the way educators teach and present information and ideas to their students. The popularity of Pinterest has even inspired other image-based social media sites such as Learnist. After developing a Pinterest account for the library, a list of best practices were created. The library looked at copyright considerations and developed a series of questions to help us determine whether to pin or repin an image. This article will look at how Pinterest can be used in libraries and higher education, and some of the copyright considerations involved in using this image-driven site.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.014 |
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".