Bibliographic record
Abstract
This dissertation examines the afterlives of violence in queer Asian/American and diasporic visual and performance culture. As a broad response to the conjuncture of the 2020 COVID-19 pandemic and the renewed attention to anti-Asian violence, this project rethinks violence beyond its familiar terms as a scene of spectacular, individual, and repetitive injury. This dissertation offers a critique of the dominant protocols of documentation and representation of violence, what I refer to as the aestheticization of violence. It questions how particular protocols and methods such as counting, measurement, and visualization, shape and delimit our collective sensibilities and knowledge of scenes of harm. In rearticulating anti-Asian violence beyond normative imaginaries and practices of liberal justice, including entrenched practices of policing, prisons, and criminalization, I rethink the dominant liberal forms of Asian American identity and its politicization. Working against a narrow and individual notion of ‘hate’ violence, the archives I unpack deploy a queer Asian/American critique of disparate sites of violence and loss. These sites expand the purview of what anti-Asian violence mean. Each chapter contends with how the contests over life and death expose and rethink how human life is valued and devalued through racial, sexual, and gender differences. Queerness is central to this project because its centering of messy intimacies that traverse geographies, histories, and subjects that unsettle the category of Asian American under neoliberal multiculturalism, a crucial precursor for unmaking anti-Asian violence beyond the state. The artists and queer aesthetics I unpack defamiliarize various sites of (non)spectacular violence: the U.S. military camptown in South Korea, the Atlanta Spa Shootings, the Asian American fraternity, the AIDS epidemic, and the Korean diaspora in Canada. As a dissertation that grapples with the structures and conditions that at times produce life-shattering violence, this work ponders on what remains and how marginalized peoples endure. It follows the ethical and political struggles to creating something a new from violence’s wake.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.043 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".