The Invisibility of the Asian American Identity in North American Bioethics
Bibliographic record
Abstract
This Perspective sheds light on the major barriers behind Asian American invisibility in bioethics, such as the inconsistencies in defining a coherent identity in North American society, the systemic mechanisms of invisibility from history to healthcare based on immigration status, and scarcity of bioethics literature about their perspectives. The consequences of Asian American invisibility in bioethics and healthcare practises are also discussed, including a reflection of the interconnected mechanisms between academic bioethical theory and healthcare practises that perpetuate Asian American invisibility. Finally, recommendations are provided to begin a critical reflection of bioethical theory with Asian Americans in mind. This serves as the stepping-stone for future discussions on Asian American bioethics, to highlight a missing perspective in North American race-based activism regarding the equity, diversity, and inclusion of an underrepresented demographic.
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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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.043 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".