“Thugs-for-Hire”: Subcontracting of State Coercion and State Capacity in China
Bibliographic record
Abstract
Using violence or threat of violence, “thugs-for-hire” (TFH) is a form of privatized coercion that helps states subjugate a recalcitrant population. I lay out three scope conditions under which TFH is the preferred strategy: when state actions are illegal or policies are unpopular; when evasion of state responsibility is highly desirable; and when states are weak in their capacity or are less strong than their societies. Weak states relative to strong ones are more likely to deploy TFH, mostly for the purpose of bolstering their coercive capacity; strong states use TFH for evasion of responsibility. Yet the state-TFH relationship is functional only if the state is able to maintain the upper hand over the violent agents. Focusing on China, a seemingly paradoxical case due to its traditional perception of being a strong state, I examine how local states frequently deploy TFH to evict homeowners, enforce the one-child policy, collect exorbitant exactions, and deal with petitioners and protestors. However, the increasing prevalence of “local mafia states” suggests that some of the thuggish groups have grown to usurp local governments’ autonomy. This points to the cost of relying upon TFH as a repressive strategy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".