Gender and Racialized Gender Issues During North American Chinese Learners’ Social Networking in China
Bibliographic record
Abstract
This research critically investigated North American female Chinese learners’ language learning experiences in China, particularly those related to their investment and gender identity negotiation. The research findings showed that although the majority of the participants were impressed by the gender equality in Chinese labor market, sexism still had a profound influence on their daily social networking with the local people. To begin with, the local judgmental discourse on women’s physical appearance imposed rigorous and homogeneous aesthetic standards on foreign females, especially the dark skin participants. Besides, the pervasive appreciation of “obedient and vulnerable” women positioned the female CFL learners into a submissive status during their interaction with male authorities. Furthermore, the commodification of women and marriage in post-socialist Chinese social networks reinforced the gender inequality and social hierarchy, which resulted in ideological and social barriers between the participants and local interlocutors. Last but not least, due to the racialized prejudice of permissive western women, some participants suffered from sexual harassment and assaults. It seriously influenced their sense of security and motivation of public social networking. (This proposal was accepted for last congress but I did not make it, so I resubmit it this year.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".