EXPANDING THE NARRATIVE ON ANTI-CHINESE STIGMA DURING COVID-19 - Initial Report.pdf
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the geographic origins of the first major outbreak of COVID-19 in Wuhan, China, there have been reports of Asians around the world experiencing discrimination, xenophobia, or racism. Such reports have been prevalent in Toronto, Canada and in Nairobi, Kenya, two global urban centres that have significant Chinese diaspora communities. Discriminatory actions have ranged from outright physical aggression to subtle microaggressions. While reports (both media and academic) have highlighted such incidents, we argue that when the conversation starts and stops at the reporting of experiences of stigma, the narrative remains the victimization of the community. While the emerging story of the instances of COVID-19 stigma and discrimination are only one aspect of this story, other aspects include a deeper understanding of the community itself along with an awareness of the capacity the Chinese diaspora community brings forward to help us all overcome COVID-19. By better understanding the complexity as well as the capacity of communities, emergency managers and public health officials can better implement social countermeasures aimed at preventing the unfair targeting of specific ethnic groups during infectious disease outbreaks. <br>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.052 | 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 it