Behind the Scenes of COVID-19: The "Hidden Pandemic" of Anti-Asian Racism
Bibliographic record
Abstract
Alongside COVID-19 came a renewed onslaught of xenophobia and anti-Asian racism, marking people who are or appear to be Chinese as a target for hate-fuelled verbal and physical assaults, some resulting in serious injury or proving fatal for the victims. Using news articles published in Canada to collect data, this research explores the impact of anti-Asian racism within Canada. The findings from this research suggest an uptick in activism throughout the COVID-19 pandemic. Roughly a year after the pandemic, social movements and organizations focused on supporting those with lived experiences of anti-Asian racism and tracking and preventing anti-Asian racism have garnered large followings and support. The resurgence of anti-Asian racism due to the fear associated with COVID-19 is a testament to how we can and should do better in the future to act collectively against racism and xenophobia, by understanding why and how it emerges in order to prevent it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".