A critical discourse analysis of women's roles as mistresses in Chinese corruption news coverage
Bibliographic record
Abstract
The terms ‘mistress’ or ‘second wife’, translated as qingfu, ernai, xiaosan, have become widely used in Chinese media in recent years. This study employs critical discourse analysis to examine how the mistress/second wife is represented in Chinese corruption news coverage. In particular, this paper analyses thirty seven articles focusing on cadre-mistress relationships that were published on mainstream commercial websites (Ifeng, NetEase, Sina, and Sohu) between 2013 and 2017. The findings of this analysis show that the framing of the mistresses/second wives in these articles follows media censorship and selectively appeals to the public's negative perception of them. Specifically, the findings reveal that mistresses of corrupt male cadres are afforded no respect in corruption news coverage, and are instead portrayed as ‘toys’ of the cadres who are ‘partners’ in their corruption. Furthermore, this paper's analysis of the sociocultural context that gives rise to such discourses demonstrates the key role played by social inequalities and gender inequalities in the post-reform era. This study contributes to the literature by illustrating how mistress–cadre relationships are governed by gender inequalities in job opportunities, career development, and income levels, social inequalities in the distribution of wealth and resources, and cultural norms relating to discrimination against concubines. Thus, such relationships cannot be eliminated via public censure.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".