Bibliographic record
Abstract
The Western liberal notion of justice is generally associated with concepts such as fairness, righteousness, complete virtue, and equality. As an important virtue of social institutions, justice is a contextual notion which conveys different meanings in different cultures. Regardless of how many articles have discussed the Western concept of justice, very few have touched on its Chinese counterpart. Justice is translated into ‘gongping’ (公平） and ‘zhengyi’（正义）in Chinese. It is difficult to find a simple Chinese term to describe the complete meaning of justice. An etymological approach was taken by analyzing the structure of four relevant Chinese characters in this paper. Moreover, since the Chinese justice has been deeply influenced by Confucianism, it is necessary to explore the meaning of justice in his celebrated work: the Analects. I conclude that the Chinese concept of justice entails connotations of fair distribution, righteousness, and equality. But the Chinese version of justice focuses on the collective interest in maintaining a harmonious society. Fairness and justice are important values embodied in Chinese laws and government reports. The Chinese notion of justice emphasizes the public interest which regards individual justice as an integral part of social justice. In public health emergencies, the state interest is closely linked with global interest. Fair distribution of genomic data abroad is in alignment with the Chinese concept of justice. I argue that the Chinese view of justice should be proposed for countries to take more responsibility in genomic data sharing to find more cures to end the pandemic.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".