Bibliographic record
Abstract
This paper delves into the ongoing issue of anti-Asian racism in Canada, particularly during the Covid-19 pandemic. Despite being a diverse country, Canada has a long-standing history of discrimination towards people of Asian heritage. The Covid-19 pandemic has only exacerbated this issue, with a significant increase in reported crimes and incidents of racism towards Asian or Asian-appearing individuals. The paper focuses on identifying and interpreting the most relevant data from various sources on anti-Asian racism in Canada during the pandemic. The author aims to compare and contrast these data sets to understand the underlying trends and factors that contribute to anti-Asian racism in Canada. However, the author notes the challenges of relying on available data sets to inform the public and policymakers. Officially collected crime statistics and non-official online self-reporting data have their limitations in accurately reflecting the scope of anti-Asian racism in the country. The paper concludes that accurate statistics are essential in combating anti-Asian racism in Canada. However, the lack of reliable data is concerning. The author emphasizes the importance of continuing the search for better ways to collect accurate statistics while being cautious in using existing data to avoid misleading the public and policymakers. Overall, this paper highlights the urgent need for Canada to address the issue of anti-Asian racism, particularly in the wake of the Covid-19 pandemic. It is a call to action for policymakers, activists, and the public to work together towards creating a more inclusive and accepting society.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| 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".