Building Critical digital diasporic spaces: digital memes and physical art in collective liberation
Bibliographic record
Abstract
Asian diasporas gather in digital spaces that transcend national boundaries and have created an aesthetic that reflects the tensions, politics and subjectivities of diaspora. The internet and especially social media sites have created important digital gathering spaces for Asian diasporic users to negotiate and reify communities and identities. In this reification, an aesthetic has formed around the principles of pleasure. This aesthetic eschews difficult and complex conversations on race, solidarity and other social justice to focus on finding a place within the settler national project instead. This article seeks to analyze the pedagogies of shame inherent in the memes created in the group subtle asian traits and suggests an affirmative movement towards a pedagogy of (be)longing. Instead, I turn to the art of queer, East Asian diasporic artist Lan “Florence” Yee, who proposes a relational and critical aesthetic that resists white hetero-patriarchal settler nationalism towards one rooted in collective liberation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".