Dismantling Racism Against Black, Indigenous, and People of Color across the Substance use Continuum: A Position Statement of the Association for Multidisciplinary Education and Research in Substance use and Addiction
Bibliographic record
Abstract
The Association for Multidisciplinary Education and Research in Substance Use and Addiction (AMERSA) acknowledges that racism profoundly affects persons who use alcohol and other drugs. Racism's deadly effects compounded with other social determinants of health result in a cascade of negative impacts. The AMERSA Board of Directors (BOD) proposes an initial set of strategies to promote diversity, equity, and inclusion using a framework that speaks to four key AMERSA experiences: engagement, education, mentorship, and leadership. Through these strategies, AMERSA commits to promoting equity and inclusion to dismantle the individual, institutional, and structural racism that has permeated the United States for centuries.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.002 | 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".