Bibliographic record
Abstract
Trafficking of women for marriage is recognized as a modern-day slave trade, although it has a long history in China. The reasons for its resurgence in China include, but are not limited to, patriarchal values, state-tolerated sex discrimination, vulnerability of women and the transformation of socio-economic situations. Accordingly, the task of eradicating the trafficking in women involves combating feudal and patriarchal assumptions about male dominance and male supremacy; building up the confidence and dignity of the gender of female; systematic governmental and international support of issues of importance to women; restructuring legal systems where they are still imperfect; adjusting economic systems so that women are never exploited economically; strengthening fundamental and higher education of women; regulating objectifying and pornographic media images of women; and developing ways in which men and women can relate without either dependency or dominance. The author examines and evaluates both the strengths and weaknesses of the current criminal justice policy of the Chinese government against trafficking practices. She then argues that the existing policy against trafficking is insufficient and ineffective and needs to be reformed in a number of aspects as suggested. In this article, the author takes a multi-disciplinary approach, including historical, cultural, economic, ideological, sociological and legal study.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".