The Impact of Censorship of LGBTQ Content on Xiaohongshu on Queer Expression
Bibliographic record
Abstract
This essay delves into the impact that the censorship of LGBTQ content on Xiaohongshu can have on the expression of queer individuals. This research conducted an analysis of the censorship mechanism of Xiaohongshu, collected tags commonly used by LGBTQ users, and proceeded to analyze the data. Subsequently, the examination of the connection between the causes and effects of the adjustments made at the societal level is done, as well as the stereotypes surrounding the LGBTQ community. Tumblr is being used as an example of how it can help LGBTQ members to find their identity, find peers and friends, and locate communities. All in all, it is concluded that LGBTQ members’ attempts to pass censorship can facilitate engagement with the public, which may dispel stereotypes. However, it is essential to acknowledge that these adjustments may also inadvertently create new stereotypes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".