Social Psychological Analysis of Online Gender Discourse and Gender Relations
Bibliographic record
Abstract
With the rapid expansion of social media platforms, online gender discourse has become increasingly prominent in shaping contemporary gender relations, particularly in the Chinese digital context, where unique political and commercial constraints create distinctive patterns of feminist expression. This paper, through a method of literature review and case study analysis, explores how social psychological mechanisms interact with technological infrastructures to shape gender discourse production and circulation on Chinese social media platforms. The study examines two cases in particular: the Gender Watch Women's Voice experience, which serves to demonstrate counter-discourse resistance strategies, and the Mimeng phenomenon, which serves to illustrate neoliberal feminist commercialization. The conclusion of the paper suggests that digital gender discourse functions through "algorithmic-psychological feedback loops," where platform architectures amplify social psychological processes, including group polarization, social identity formation, and collective efficacy building. The research indicates that identical technological features can result in divergent outcomes across different political and commercial contexts. Paradoxically, platform censorship has been observed to strengthen feminist counter-discourses, while commercial platforms co-opt feminist language to reinforce patriarchal structures.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".