Information control by public punishment: The logic of signalling repression in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When does repression of online expression lead to public punishment of citizens in China? Chinese social media is heavily censored through a system of intermediary liability in which the government relies on private companies to implement content controls. Outside of this system the Chinese authorities at times utilize public punishment to repress social media users. Under China’s regulatory environment, individuals are subject to punishment such as fines and detention for their expressions online. While censorship has become more implicit, authorities have periodically announced cases of repression to the public. To understand when the state escalates from censoring online content to punishing social media users for their online expressions and publicizes the punishment, we collected 468 cases of state repression announced by the authorities between 1 January 2014 and 1 April 2019. We find that the Chinese authorities most frequently publicize persecutions of citizens who posted online expression deemed critical of the government or those that challenged government credibility. These cases show more evidence of the state pushing the responsibility of ‘self-regulation’ further to average citizens. By making an example of individuals who post prohibited content even in semi-public social media venues, the state signals strength and its determination to maintain authority.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 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 it