The politics of<i>Pai Ma Pi</i>: flattery as empty signifiers and social control in a Chinese workplace
Bibliographic record
Abstract
This article analyzes the practice of pai ma pi in a Chinese workplace in order to examine the recent transformation of subject formation and the political economy in China. Pai ma pi refers to any benevolent practice a subordinate directs toward his or her superiors to seek favor, protection, or other benefits. More than flattery or brownnosing, pai ma pi in a socialist workplace serves as an empty signifier; workers use it to combat the void created by their uncertainty about their position in relation to the disproportionate power of the all-encompassing, autocratic system, a power crystallized in the need for workers to maintain a clean record in their classified dang'an (personal dossier) to survive. However, the gradual breakdown of the socialist work unit system caused by neoliberal economic restructuring appears to diminish the practice of pai ma pi. The article, nevertheless, illustrates that pai ma pi both nurtures and challenges the neoliberal economy. As a strategic form of praise, pai ma pi, by aggrandizing higher-ups, intensifies the social hierarchy, and bureaucratic authority; meanwhile, it advances self-interest, which resonates with some of the neoliberal pillars in China, such as “freedom” and “self-enterprising.”
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".