The Unprescence of China’s COVID-19 Trauma and Its Impact on Social Identity
Bibliographic record
Abstract
In the psychology and literary fields, the theoretical study of trauma has received increasing attention. It is widely applied by experts and scholars across various aspects, such as war, gender and so on. To give it more practical significance, the main object of this study is to investigate the modern use of trauma by connecting it to the topic of a nation. China, the country first plagued by COVID-19—a representative modern trauma—has suffered not only physically but also mentally. This paper will analyze how trauma affects a nation by using classical theories on trauma, such as those from Sigmund Freud and Cathy Caruth. In terms of national collective trauma, new theories from Roger Luckhurst and Jeffrey C. Alexander would also be adopted. To achieve the sophisticated link between trauma and nation, the unprescence of trauma and its social identity threat to China are further discussed as main parts of this essay. This research will encourage a more rational treatment of collective trauma sufferers and calls for the realistic and practical use of the literary trauma theory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".