Authoritarianism as a Research Constraint: Political Scientists in China*
Bibliographic record
Abstract
Objective This article examines the ways the authoritarian nature of the regime in the People's Republic of China constrains the conduct of political science research. It further seeks to identify ways in which researchers have circumvented authoritarian controls. Methods The article examines existing scholarly literature and curricula pertaining to Chinese politics to identify methodological and technical tendencies in the research field. It then conducts a deeper, theoretical investigation to show how researchers exploit loopholes and blindspots in the authoritarian system to generate novel research. Results The study finds a marked propensity in the study of Chinese politics toward qualitative research. Research on local politics is considered less sensitive and thus is more prevalent than studies of the central government. Government restrictions have forced scholars to imperfect data for empirical support. Conclusion Although it is easier to generate new findings in politically open settings, the authoritarian nature of the Chinese regime does not necessarily hinder advancement in social science. Quantitative research that relies on government‐issued data is useful, but remains liable to government restriction. Qualitative and ethnographic research gives the researcher opportunities to bypass restrictions imposed by the regime. These opportunities depend upon the researcher's ability to immerse herself in the relevant communities, find reliable and context‐aware collaborators, and develop creative ways of collecting information about state behavior.
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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 | 0.030 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".