“A Self You Have Not Yet Learned How to Love” : Building Asian/Queer//Queer/Asian Possibilities Through Archival Speculation
Bibliographic record
Abstract
Archives often preserve materials that reinforce privileged identities and marginalize LGBTQIA+, BIPOC, and disabled communities. Furthermore, there is only limited theoretical work addressing how to ethically document intersectional identities, especially the dual embodiments of Asianness and queerness. Inspired by K.J. Rawson’s theorizing of accessing transgender//desiring queer archival logics, we employ critical case studies to analyze how Asian/queer//queer/Asian identities are represented in archival collections. Our study finds that Asian/queer//queer/Asian theory offers a new lens and new tools to combat archival erasure and misrepresentation resulting from heteronormativity, white supremacy, and cisgender misogyny. This article develops three critical case studies focusing on the white queer gaze toward Asian queer bodies in archives, the disidentification of Asian/queer//queer/Asian identities within archival records, and the use of archival speculation to explore Asian/queer//queer/Asian identities. This work makes both practical and theoretical contributions. Practically, we advocate for proactive archival practices that better represent such identities, avoiding essentialist representations. We also highlight the importance of embodied knowledge and the positionality of scholars and practitioners whose lived experiences centre Asian queer identities along with approaches like revisiting collections, creating reparative descriptions, and reading against the archival grain. Theoretically, we argue for archival speculation as a legitimate mode of inquiry and a process of knowledge production, positioning archives as sites that encourage disidentification.
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.062 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".