Women in the Uyghur Advocacy Movement in Canada: The Making of a Political “Activist"
Bibliographic record
Abstract
This study analyzes the life stories of three female Uyghur political activists. Born and raised in East Turkestan/Xinjiang, all three chose to emigrate to the West. Today they live in Canada, advocating for the rights of Turkic peoples in their “Homeland” and raising public awareness of the CCP’s campaign against the Uyghurs, a campaign which is currently recognized as genocidal by seven countries as well as a number of human rights organizations. This study adopts a narrative analysis of these life stories, which were collected as a form of oral history. The narratives focus on the experiences of ethnic Uyghurs living, studying, and working in China in the 1980s–2000s during the ongoing crackdowns and “strike hard” campaigns in East Turkestan/Xinjiang. Through the techniques of narrative analysis, we investigate and analyze the tensions, turning points, and motivations which led to their personal transformations and decision to become publicly involved in creating social and political change for their community. While the political statements of Rukiye Turdush, Arzu Gul, and Raziya Mahmut have been widely circulated in Canadian government and media reports, this study focuses on their personal lives and the troubling, traumatic events in their youth which triggered their choice to leave China. We ultimately argue that a narrative analysis of their stories helps us perceive these narratives as a continuation of their activism.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.067 | 0.020 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".