Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it