No fats, no femmes, no Asians: Reimagining sexual and ethnic identities of queer Asian men
Bibliographic record
Abstract
There is a common belief in the queer community that the oppressed cannot oppress others. In many ways, this support paper will document the meandering paths I took to create the short documentary No Fats, No Femmes, No Asians. But more than that, it addresses how such blatant racism and discrimination is accepted in the queer community. It is a discussion of how queers and Asians are portrayed in the media, and how the early representation of these two groups has created trauma and has become ingrained in the larger queer community. I will be borrowing from important questions raised by neocolonialists, critical race and queer theorists, as I make references to popular and queer culture. These will be my guides for a theoretical investigation of identity politics in Canada, specifically identities of queer Asian men. Using experimental, auto ethnographic, and performative documentary tactics, I will offer alternative images and different ways of presenting those images so that they cannot be taken up as another form of subjugating queer Asian men into stereotypical discourse.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".