Building an Inclusive and Antiracist Library: East Asian Library Initiatives at the University of Toronto
Bibliographic record
Abstract
This chapter examines the initiatives undertaken by the Cheng Yu Tung East Asian Library at the University of Toronto in response to the significant rise in anti-Asian violence and discrimination, particularly during the COVID-19 pandemic. With 943 reported incidents in Canada in 2021—a 47% increase from the previous year—the library has positioned itself as a vital community resource for combating systemic racism. Key initiatives include the "Guide on Anti-Asian Racism: Asian Canadian History," which provides educational resources on the history of anti-Asian racism; the "Asian Canadians: A Snapshot of History" social media series, highlighting key events and contributions; and the publication project "Asian Canadian Voices: Faces of Diversity," which celebrates the experiences of Asian Canadians across various fields. In addition, the chapter discusses public engagement events and the COVID-19 web archiving project aimed at preserving the Asian Canadian experience during the pandemic. Collectively, these efforts emphasize the library's role in fostering understanding and supporting marginalized communities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.039 | 0.008 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".