A Look in the Mirror: How the Field of Behavior Analysis Can Become Anti-Racist
Bibliographic record
Abstract
Sparked by recent events, discussions of systemic racism and racial inequalities have been pushed to the foreground of our global society, leading to what is being called the largest modern-day civil rights movement (Buchanan et al., 2020). In the past, Black, Indigenous, and People of Color (BIPOC) activists and scholars, amongst others, have evaluated and critiqued systems and organizations within our society. Nonetheless, it was not until recently that this movement was truly noticed by a greater number of people, some of whom are now further assessing how BIPOC are viewed and treated within their organization and by society as a whole (Worland, 2020). This is not only due to the increase in video evidence (e.g., released body cam footage, social media postings), but also to the previous administration’s rhetoric and political agenda (Hubler & Bosman, 2021). Police departments, educational institutions, and large companies have, for decades, been under scrutiny for their systems and practices that promote racism, inequality, and inequity. The field of behavior analysis, with its Eurocentric roots and observed lack of diversity, equity, and inclusion, is not exempt from such evaluations. It is time that we take a look in the mirror and evaluate our own professional, research, educational, and clinical practices, and work towards creating a new, more inclusive, field of behavior analysis that promotes anti-racism and cultural humility.
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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 | 0.111 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.018 | 0.105 |
| Scholarly communication | 0.035 | 0.050 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.018 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".