Sorting citizens: Governing via China's social credit system
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
Abstract China's social credit system can be examined as a governance tool which sorts citizenship behaviors into trustworthy and untrustworthy categories as part of the regime's long‐standing effort to cultivate a loyal citizenry. Based on a data set comprised of central‐level official documents, national model citizen lists, and media reports, this study qualitatively examines how the Chinese state constructs “good” and “bad” citizen ideal types. Contrary to media depictions of the system as digital totalitarianism, political behaviors are not the sole criterion for sorting citizens into categories. In fact, the state constructs “bad” (untrustworthy) citizens as those who engage in a wide range of behaviors, including financial and professional misconduct. Simultaneously, the state also uses the system to construct and cultivate “good” (trustworthy) citizens as those who publicly demonstrate loyalty to the regime. Theoretically, this study sheds light on how the world's most powerful authoritarian regime governs through a system that distributes material and symbolic capital.
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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 | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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