The Weak Yet Savvy Street-Level Police Officer
Bibliographic record
Abstract
This chapter is about how police officers engage with sex workers when they are not enforcing anti-prostitution laws against them. By focusing their enforcement efforts on low-tier sex workers, the police help create a space for the middle tier of China’s sex industry – entertainment venues and their hostesses—to thrive. I find that law enforcement officers engage actively and in myriad ways with the sex industry when they are not focused on arresting sex workers. Some of their actions are purely extractive interactions. Yet other police behavior, while still self-serving, also benefits sex workers. Making sense of police actions in this context requires shifting our framework from exclusively viewing police as powerful figures in relation to sex workers to also viewing them as street-level bureaucrats who are accountable to the local government and the vast police bureaucracy of which they are at the forefront. This approach provides a different perspective on police officers, underscoring their weakness within China’s bureaucratic system rather than their strength in relation to the sex workers. Their vulnerability vis-à-vis the state even affects how they engage with sex workers and underscores conditions under which the job security of frontline police officers in fact depends on a cooperative local sex industry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".