Naodongfang: an examination of meanings in Chinese wedding games and pranks
Bibliographic record
Abstract
This doctoral dissertation in Folklore explores a traditional, widespread wedding custom in China known as Naodongfang. When used generally, the term “Naodongfang” refers to the scope of all jokes, pranks and games played by grooms, brides, groomsmen, bridesmaids, parents, parents’ siblings and relatives during Chinese weddings. Although the custom has a long history, stretching back 2000 years, today many Chinese citizens consider its elements, such as beating grooms, humiliating brides and bridesmaids, and encouraging fathers-in-law and brides to hug and kiss each other, as bizarre, inappropriate, and even malicious. In this study I attempt to answer the questions: “Why is Naodongfang active in China today?” and “What are Naodongfang’s meanings?” I draw on published descriptions and analyses as well as my own ethnographic materials to explore economic meanings; discuss connections to Chinese concepts such as the Golden Mean, Propriety, and female reservedness; and examine Nao culture in the context of the carnivalesque. A significant focus of the thesis is the exploration of gender issues and violence associated with Naodongfang. Reports of injuries, sexual harassment, and sexual assault experienced by both female and male participants have made the custom controversial. Thus, the last three chapters explore connections of Naodongfang with Chinese feminism, patriarchy and constructions of masculinities. My intention is not to argue whether Naodongfang is good or bad. Rather, I hope this thesis provides new ethnographic materials and analyses on Naodongfang in a way that contributes to a larger conversation of wedding games and pranks and their deeper meanings. I argue that although this custom is criticized and stigmatized by many, it still holds value for some individuals and their communities. These meanings are multifaceted. Naodongfang allows participants to laugh and joke and to step out their everyday lives. This liminal time, with its freedom from usual decorum, can bring participants together or it can pit them against each other as guests compete for red packets or exert pressure on, or exact revenge against, the bridal couple or bridal party. Although Naodongfang can feel like an act of resistance towards authorities, it also reinforces existing family and clan power structures, as well as gender stereotypes, and promotes heteronormativity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| 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".