Bibliographic record
Abstract
When news media in the UK and US discuss China’s surveillance networks, it is often to imply that the Chinese government is creating a “techno-authoritarian state” to track and monitor its citizens. News outlets, however, are missing a larger point. The specific problem with China’s surveillance apparatus is not that it is technologically “totalizing” and “intrusive,” but that it relies on a newly digitized information platform that connects surveillance subjects to information about their households and family members, allowing the simultaneous identification and monitoring of everyone in each kinship network. Referred to as the Household Register or hukou, this platform is the backbone of China’s “surveillant assemblage” (Haggerty and Ericson 2000). Until the late 1990s when it was digitalized, hukou was an individually separate and distinct surveillance system that contained both general and detailed information about a household and its members. With digitalization, hukou became a platform that integrates different computer systems and databases. CCTV surveillance that involves facial recognition and Internet surveillance practices are connected to, and supported by, information from hukou. In the case of CCTV surveillance, cameras equipped with facial recognition features match the face of surveillance subjects with their ID and trace them back to their families. As for Internet surveillance, the connection between hukou and surveillance subjects happens via telephone number. Access to the Internet and social media platforms such as WeChat, SinaWeibo, and e-mail services requires a telephone number purchased with a government-issued ID card, which is connected to a household register and, therefore, the telephone card owner’s family. Chinese law enforcement’s ability to treat individual Internet users also as “collective units” represents the most distinctive feature of Chinese surveillance, an unlimited source of coercion for the Communist Party to reproduce itself as the ruling party.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".