Content Analysis of Libraries’ Instagram Posts: Cultural Collection, Activities, and Preservation of Cultural Heritage
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
Abstract Libraries’ roles and contributions in promoting and raising awareness of culture and cultural heritage to support the sustainability of cultural life can be strengthened by utilizing social media platforms, including Instagram. However, there is a gap in studies and research relating to how academic libraries reflect their cultural functions through social media, i.e., Instagram. This paper provides a content analysis of academic libraries’ Instagram accounts at three academic libraries located in the United States, Canada, and the United Kingdom. These libraries represent their universities’ concerns with promoting sustainable development goals, specifically Sustainable Development Goal 11 (make cities and human settlements inclusive, safe, resilient, and sustainable). This study analyzed and categorized the Instagram posts of academic libraries related to culture and cultural heritage to answer the following research question: how do academic libraries reflect their cultural functions through social media, i.e., Instagram? The results show that the academic libraries studied here considered reflecting their cultural functions through social media by informing users about various cultural events, collections, facts, and news on Instagram.
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 | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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