Making Sense of the Uyghur Genocide Through Religious Theodicy
Bibliographic record
Abstract
This study aims to explore how some Uyghurs in Canada and Türkiye[1] interpret the ongoing Uyghur Genocide through a religious lens, as a form of theodicy, an explanation for evil and suffering.[2] The article is based on data collected from three different sources. The first source is a diary which recorded discussions of individual Uyghurs participating in an online class on the Qur’an taking place in 2021. The second source comes from interviews with two prominent Uyghur Imams who reside in Canada and Türkiye, and the third is an interview with a young Uyghur political activist residing in Canada. From their narratives, we have identified four distinct yet interrelated perspectives, which we have categorized into four categories: gnostic, mythic, apocalyptic, and mystery. Most of the Uyghur voices in our study understand and present the ongoing genocide in East Turkestan[3] as a divine test. Despite understanding the ongoing genocide as a test by Allah, the aspect of divine punishment appears to be downplayed, and instead what is emphasized is the goodness of Allah who seeks to teach His servants lessons so that they may achieve greater virtues in both this life and the afterlife. Our analysis has led us to the conclusion that Uyghur Islam seems to offer its adherents a sense of optimism and, for Uyghurs living in the diaspora, a means to move forward. This is in stark contrast to other forms of Islamic theodicy which focus more on the aspect of suffering. [1]. This spelling follows the Turkish government’s 2022 request for Turkey to be referred to as Türkiye – its spelling and pronunciation in Turkish. See “UN Agrees to Change Turkey’s Official Name to ‘Türkiye,’” Al Jazeera, June 2, 2022, https://www.aljazeera.com/news/2022/6/2/un-registers-turkiye-as-new-country-name-for-turkey. [2]. John Hick, Evil and the God of Love (New York: Palgrave Macmillan, 2007) and Mark S. Scott, “Theodicy at the Margins: New Trajectories for the Problem of Evil.” Theology Today 68, no.2 (2011): 113–204, https://doi.org/10.1177/0040573611405878. [3]. In this article we will use “East Turkestan” or “the Uyghur Homeland” to refer to the Xinjiang Uyghur Autonomous Region of China. The Chinese term “Xinjiang” means “New Frontier,” “New Borderland,” or “New Territory,” a designation which was given to the region by the Qing dynasty in 1884, so it is politically fraught for Uyghurs because it justifies the displacement of its occupants by Chinese settlers. As the present study focuses on the Uyghur perspective, the preferred Uyghur terms for the region, given above, will be used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".