Organizing for racial and economic justice during the COVID-19 Pandemic: Experiences from the Chinese Canadian community in Toronto
Bibliographic record
Abstract
In this article, I share my experiences involved in organizing around racism and for social and economic justice broadly during the COVID-19 pandemic. This article specifically focuses on some of my experiences involved in a small, grassroots, community-based organization with a focus on anti-racism, workers’ rights and social justice as the Executive Director at the Chinese Canadian National Council Toronto Chapter (hereafter CCNCTO) from July 2018 to May 2021. CCNCTO is an organization of Chinese Canadians in the City of Toronto that promotes equity, social justice, inclusive civic participation, and respect for diversity. CCNCTO has an extensive history working in racial justice and advocacy including being a key part of the coalition for winning redress for Chinese head-tax survivors from the Canadian government.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.077 | 0.024 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".