Bibliographic record
Abstract
In genocide, both women and men suffer. However, their suffering has always been different; with men mostly subjected to torture and killings, and women mostly subjected to torture and mutilation. These differences stem primarily from the perpetrators' ideology and intention to exterminate the targeted people. Many patriarchal societies link men with blood lineage and the group’s continuation, while women embody the group’s reproductivity and dignity. In the ongoing genocide against the Uyghurs and other Turkic Muslims in East Turkistan, the ideology of Chinese colonialism is a root cause. It motivates the targeting of women as the means through which to destroy the reproductivity and the dignity of the people as a whole. It is a common misunderstanding to associate genocide with only mass killings, and the current lack of evidence for massacres has led some to prematurely conclude there is no genocide. But this overlooks the targeting of women, which is also a prominent part of the definition of genocide laid out in the Genocide Convention. State policy in China intentionally targets Uyghur and other Turkic women in multiple ways. This dossier is focused on analyzing China’s targeted policies against Uyghur women and their “punishment,” as rooted in part in ancient Chinese legalist philosophy. In doing so, this dossier contributes toward further exposing Chinese colonialism and the genocidal intent now in evidence.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".