Social death with nature: a comparative analysis of the Cambodian and Canadian settler genocides and life-death worldings
Bibliographic record
Abstract
To date, genocide studies has maintained certain asymmetric typologies that demarcate physical from cultural forms of destruction and gendered violence as distinct from homogenous narratives of mass physical violence. In this dissertation, I argue that assumptions underlying these binaries are rooted in anthropocentrism. In genocide studies this translates into a narrow focus on physical human existence and destruction that overlooks the complex and multiple ways through which an ethnic or cultural group imagines itself, maintains collective identity, and renegotiates its social relations in and through genocide, as part of a human-ecological assemblage. Furthermore, this limited focus obscures the gendered embodiment of genocidal violence and the depth to which gendered bodies harbour pain and trauma. To date, if gendered violence is noted, most often it is relegated as secondary to the preservation of human life. I transverse these asymmetries to examine the dynamism of group life and the ecological and gendered materialities and social relations that give meaning to the individual and group’s existence. Protecting human life will always be a significant focus of genocide studies and prevention. However, when we isolate human beings from the multiple relations that bring meaning to their lives, we miss the opportunity to more fully grasp what makes genocide possible, what genocide destroys, and the complex harms it leaves in its wake. In this dissertation, I examine Khmer Cambodian and Anishinaabeg and Cree collective identities as dynamic and evolving relational assemblages. By doing so, I open genocide studies to several insights from Indigenous and settler colonial studies and feminist new material theories: 1) An understanding of the group and its attempted destruction beyond anthropocentric terms; 2) A non-linear and dynamic genocidal temporality; 3) A more expansive understanding of and response to gendered and relational harms in genocide. Importantly, the key argument that I make in this dissertation is that genocidal destruction operates on multiple fronts; ecological and gendered materialities and social relations are mutually targeted in genocide—at times conjointly, at other times, unevenly. Therefore, this dissertation provides insight into an assemblage (rather than yet another asymmetric typology) of genocidal destruction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".