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.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".