Bibliographic record
Abstract
Research on the identification of obscene materials in China focuses on the current state of legislation, judicial practice, and potential improvements. At the legislative level, China's definition of obscene materials is governed by multiple regulatory frameworks, including criminal law, administrative regulations, and departmental rules. Criminal law establishes the core criterion of "obscenity" , while administrative regulations and departmental rules provide detailed behavioral norms and appraisal procedures, balancing the suppression of criminal activity with the protection of cultural rights.In judicial practice, however, challenges arise due to the abstract nature of key criteria such as "obscenity" and "explicit depiction of sexual conduct," leading to blurred boundaries in identification. The author analyzes the legal interests infringed upon by obscene materials and draws on the experiences of the UK, the US, and Canada, where the protection of public morality is central. These jurisdictions have developed objective assessment systems—such as the community standards and the triple standard—to determine obscenity, offering valuable insights for China’s legal framework.The conclusion suggests that China’s legislation should clearly define the core characteristic of "obscenity," while the judiciary should establish a dual-review mechanism combining professional appraisal with societal consensus. The assessment should be based on social harm, striking a balance between public decency and cultural prosperity.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".