Opinion ∙ Privacy in China: Legal Culture, Literacy, and Imaginaries
Bibliographic record
Abstract
In many parts of the world, citizens leave data traces when they conduct internet searches, post on social media, send messages, make electronic payments, or go by facial recognition cameras.In China, the scale of the data being collected, in a sociotechnical environment where the use of cash is fast disappearing and social media platforms such as WeChat are the fabric of everyday life for personal, social, and work purposes, heightens privacy and data protection issues.In this text, I wish to introduce readers to field work that I have conducted in China to understand Chinese citizens' privacy and surveillance imaginaries and how they live with such an intense exposure.I hope this brief note will foster interest for my book Living with Digital Surveillance in China: Citizens' Narratives on Technology, Privacy and Governance recently published in the Routledge Studies in Surveillance series. 1 This monograph draws on in-depth research interviews I have conducted in Chengdu, Shanghai, and Beijing in 2019, a diary of daily observations during the time I spent there and in travels in the Western provinces of Shaanxi, Gansu, Qinhai, Xinjiang, and Sichuan, and extensive cross-disciplinary documentation. I. The Legal Culture of Privacy in ChinaIn China, legal cases regarding privacy matters are more often been based on the right to reputation or the right to portrait than on the right to privacy. 2 This emphasis on reputation may reflect the philosophical roots of privacy, which tend to subordinate the personal realm to the public realm. 3 While both Confucianism and Taoism emphasize self-cultivation, they do not frame self-cultivation as gaining individual autonomy and intimacy or developing values and beliefs distinct from social norms. 4 However, privacy protection has gained traction over the past decades.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".