Racism, structural racism, and the American Association for Anatomy: Initial report from a task force
Bibliographic record
Abstract
In 2021, the American Association for Anatomy (AAA) Board of Directors appointed a Task Force on Structural Racism to understand how the laws, rules, and practices in which the Association formed, developed and continues to exist affect membership and participation. This commentary is the first public report from the Task Force. We focus on African Americans with some comments on Jews and women, noting that all marginalized groups deserve study. Through much of its 130 year history, some members were an essential part of perpetuating racist ideas, the Association largely ignored racism and had some practices that prevented participation. The Task Force concluded that individual and structural racism within the AAA, combined with the broader social context in which the Association developed, contributed to the current underrepresentation of African Americans who constitute 4.1% of the membership even though 13.4% of the U.S. population is Black. Intentional efforts within the AAA to reckon with racism and other forms of bias have only begun in the last 10-20 years. These actions have led to more diverse leadership within the Association, and it is hoped that these changes will positively affect the recruitment and retention of marginalized people to science in general and anatomy in particular. The Task Force recommends that the AAA Board issue a statement of responsibility to acknowledge its history. Furthermore, the Task Force advocates that the Board commit to (a) sustaining ongoing projects to improve diversity, equity, and inclusion and (b) dedicating additional resources to facilitate novel initiatives.
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.
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 | 0.034 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".