Cognitive Limits, Herding Effects, and Group Segregation: Stigma Generation and Destigmatization Pathways of Hepatitis B Patients in China
Bibliographic record
Abstract
Hepatitis B, once the number one epidemic in China, afflicted nearly 300 million Chinese people. Today, it is still the number one killer of infectious diseases. Due to the strong pathogenicity of hepatitis B, many people in mainland China are scared of the disease. They are afraid to avoid it, leaving many hepatitis B patients and even carriers to suffer much discrimination. They are typically denied the right to work, live and marry because of hepatitis B. As a result, many people with hepatitis B and those who are carriers of the virus suffer from stigma. In this paper, the authors analyze data on hepatitis B patients and pages, as well as data published by the Chinese Ministry of Health on hepatitis B, to connect the dots between the daily lives of hepatitis B patients and the discrimination they face in China. This will help researchers and the general public to understand hepatitis B and to reduce or even eliminate the fear of hepatitis B. Nowadays; the Chinese government has begun to help the general public understand and eradicate the fear of hepatitis B through health legislation, daily publicity, and education, to give these people a fair and just environment to live in.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".