Content Analysis of Libraries’ Instagram Posts: Cultural Collection, Activities, and Preservation of Cultural Heritage
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.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".