Crisis Management of Group Events in Chinese Universities Under the Background of Internet: A Literature Review
Bibliographic record
Abstract
With the general increase in the degree of education coverage of national, the number of colleges has risen sharply and the scale expands apparently which leads to the frequent occurrence of mass incidents in colleges and universities, resulting in the loss and harm of public property, personal security and other aspects in varying degrees. Therefore, in recent years, the crisis management of college group events has become a hot spot for many scholars. Under the condition of combining the background of the actual times and the social environment, many scholars put forward the multi-angle crisis management mechanism and theoretical method. However, with the rapid development of the Internet in recent years, great changes have taken place in both the group events of universities and the means of information dissemination. Therefore, it is very important to find a complete flow mechanism for college group events under the network background that adapts to the background of the times and can be applied to the actual situation of our country. Reviewing and summarizing the methods and countermeasures proposed by many scholars, this paper puts forward the views and suggestions on the management mechanism under the Internet background.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".