“South Asians don't count as Asian”: Using Reddit to Explore Discussions of Anti‐Asian Racism within the South Asian Diaspora
Bibliographic record
Abstract
In 2019, the COVID‐19 pandemic impacted people across the world like no other disease of its kind. The origins of the virus were identified to be from Wuhan, China, which led to East Asians becoming the targets of racist attacks and discrimination. “Other” Asian subgroups such as South Asians and Southeast Asians have also experienced hate crimes targeted toward them (CCNCTO, “Another Year: Anti‐Asian Racism Across Canada Two Years Into the COVID‐19 Pandemic.” 2022). Yet, their voices have largely been missing from conversations on anti‐Asian racism. Using thematic analysis, I explore how South Asians construct their positionality within conversations of anti‐Asian racism. I examine the use of terms such as “anti‐Asian,” “Asian racism,” “racism,” “hate crime,” and “discrimination” in 209 posts and 20, 388 comments between 2020 and 2022 within r/ABCDesis, a Reddit community formed by and for the South Asian diaspora, primarily residing in the United States, and Canada. Findings suggest that while most members have personally not been impacted by anti‐Asian racism, they are wary of being the next target. Redditors expressed the need for greater solidarity with East Asians and POC's, and yet demonstrate how ethnic ambiguity and complex intergroup relations can pose difficulties when expressing positionality within discussions of anti‐Asian racism.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| 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".