Сінофобія та кіберагресія в умовах глобального виклику COVID-19: комунікаційні аспекти
Bibliographic record
Abstract
Vovkoboi A., Butyrina M. COVID-19 vs Globalization: how ethnic Chinese became victims of verbal cyber aggression This article deals with the phenomenon of verbal cyber aggression that occurred due to the COVID-19 pandemic. Presently, Chinese immigrants all over the world are being mistreated and blamed for the spread of the new coronavirus. Social media platforms are filled with the racist posts encouraging various forms of discrimination. Therefore, the investigation of the online toxicity levels is a highly topical issue, especially in the USA, the United Kingdom and Canada, which are English-speaking countries with the highest rates of total confirmed COVID-19 cases. More than 2000 comments on the twits of D. Trump, B. Johnson and J. Trudeau were obtained by using the content analysis method. By applying the descriptive and comparative methods the specific aspects of the racist expressions were considered. Within the analyzed examples, the levels of toxicity were found to vary from 4 % (UK) to 13 % (CAN) and to 48 % (USA). Even though these levels seem to be below the average, the tendencies of their growth are quite alarming. The study novelty lies in the analysis of the present-day racism outburst and means of its spread. Practical significance of this study implies a possibility of usage of its findings for further research and analysis – both theoretical and practical.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".