Asian Conscientization: Reflections on the Experiences of Asian Faculty in Academic Medicine
Bibliographic record
Abstract
ISSUE: Asians have experienced a rise in racialized hate crimes due to the anti-Asian rhetoric that has accompanied the COVID-19 pandemic. However, there has been little acknowledgement of anti-Asian discrimination within the medical education community. While anti-Asian hate is not new or unfamiliar to us, four authors of Asian descent, it has given us an opportunity to reflect on how we have been complicit in and resistant to the larger racial narratives that circulate in our communities. EVIDENCE: In this article, we provide a brief history of Asians in the Americas with a focus on anti-Asian hate. Next, while presenting stories from the perspective of Asian medical education researchers who were born/have settled in the U.S. and Canada, we take the opportunity to reflect on how our personal experiences have shaped our perceptions of ourselves, and the representations of Asians in the field of medicine. IMPLICATIONS: We hope to create awareness about how stereotypes of success tied to Asians can be used as a tool of oppression creating strife between Black communities, Asian communities, and other people of color. There is a need to develop critical consciousness to address the issues of equity in academia and in clinical practice.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.041 | 0.042 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".