Identifying university opinion leaders during the global crisis: information communication on official websites
Bibliographic record
Abstract
This study investigates the news articles about the global crisis published by university official websites, aiming to identify opinion leaders from world-class universities. Based on the characteristics of opinion leaders, content analysis is used to analyze the case of COVID-19. The professionalism of all information is assessed through qualitative analysis, and activity and centrality are quantified by descriptive statistics and social network analysis, respectively. The findings of this study indicate that these three criteria can be used to assess universities. University opinion leaders show the characteristics of high professionalism, high activity and high centrality. Influenced by the institutional traits of world-class universities, university opinion leaders are divided into different types. Playing a comprehensive and multidimensional role may enable the public to quickly recognize their contributions through multiple channels, thereby gaining wide recognition. Moreover, extensive information sharing facilitates effective coordination of crisis management activities with global institutions. Some iconic brand achievements enhance their overall influence, producing a halo effect. This study is one of the first exploratory attempts to identify university opinion leaders, integrating communication theory with practical application in higher education.
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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.009 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".