The use of social media by higher education institutions
Bibliographic record
Abstract
Higher education institutions have been using, to an increasing extent, various marketing methods and tools, which are becoming a decisive factor in building their competitive advantage and achieving success. In order to initiate and maintain long-term relationships with their communities and to conduct other marketing activities, higher education institutions have been increasingly often using social media, which has enabled them to actively create their image. The aim of this study is to utilize big data methods and tools to measure the scale of the use of social media by the higher education sector. The research carried out in the first quarter of 2019 demonstrates that large higher education institutions, i.e. those with over 1696 students (according to the adopted classification), use social media to communicate current news to a larger extent than the smaller ones. A significantly smaller percentage of mediumsized higher education institutions (223-1695 students) and small ones (up to 222 students) have accounts in social media, thus failing to take full advantage of the potential of these media. Higher education institutions use social media mainly to promote events they organise.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| 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".